Fungal Community Diversity and Structure from Cave Mineral Surfaces and Bat Guano in Kartchner Caverns, Arizona

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Authors Vaughan, Michael Joe Steven

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FUNGAL COMMUNITY DIVERSITY AND STRUCTURE FROM CAVE MINERAL SURFACES AND BAT GUANO IN KARTCHNER CAVERNS, ARIZONA

Michael Joe Steven Vaughan

______

A Dissertation Submitted to the Faculty of the

SCHOOL OF PLANT SCIENCES

In Partial Fulfillment of the Requirements For the Degree of

DOCTOR OF PHILOSOPHY

In the Graduate College

THE UNIVERSITY OF ARIZONA

2012

THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE

As members of the Dissertation Committee, we certify that we have read the dissertation prepared by Michael Joe Vaughan entitled

“Fungal community diversity and structure from cave mineral surfaces and bat guano in Kartchner Caverns, Arizona” and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy.

Barry M. Pryor______Date:

A. Elizabeth Arnold______Date:

Peter J. Cotty ______Date:

Marc Orbach______Date:

Judith L. Bronstein______Date:

Final approval and acceptance of this dissertation is contingent upon the candidate’s submission of the final copies of the dissertation to the Graduate College.

I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement.

Barry M. Pryor ______Date: Dissertation Director

STATEMENT BY AUTHOR

This dissertation has been submitted in partial fulfillment of requirements for an advanced degree at the University of Arizona and is deposited in the University Library to be made available to borrowers under rules of the Library.

Brief quotations from this dissertation are allowable without special permission, provided that accurate acknowledgment of source is made. Requests for permission for extended quotation from or reproduction of this manuscript in whole or in part may be granted by the head of the major department or the Dean of the Graduate College when in his or her judgment the proposed use of the material is in the interests of scholarship. In all other instances, however, permission must be obtained from the author.

SIGNED: Michael Joe Steven Vaughan

ACKNOWLEDGEMENTS

I would like to begin by thanking my advisor, Barry Pryor, who provided me with an opportunity to turn a love of caves and fungi into a degree. I will always be thankful for the time I was granted to work in his laboratory. I also would like to express gratitude to my committee members, Betsy Arnold, Marc Orbach, Peter Cotty, and Judie

Bronstein. I have grown enormously in the past 5 years thanks to their combined efforts.

I also am thankful for the collaborations that I have developed during my tenure at the University of Arizona. First, thanks to the Kartchner Microbial Observatory team and the members of Dr. Raina Maier’s lab, Antje, Marian, and Julie. Our discussions concerning research questions and methods were invaluable. The bioinformatics help I received from Will and Cari was much appreciated. Finally, my work would not have been possible without the help of my friends at Arizona State Parks and Kartchner

Caverns State Park. Many thanks are due to Bob Casavant, Ginger Noland, Steve

Willsey, Mary Kumiega, and KC Curtis. I look forward to much collaboration in the future. I also thanks my funders, the NSF, Science Foundation Arizona, and BioME.

I would also like to give a heartfelt thanks to all of my friends and the lab members who have helped me through graduate school. Many thanks Ming and Geoff,

Dr. Ravi, Daniel, Kojun, Monica and Chad, and everyone else in the Plant Pathology

Department. Many thanks as well to Matt Trausch and Liz Prohaska, who served as my teaching mentors in the BioME program. Finally, I owe many thanks to my family.

Without you, I would never have made it. Thank you.

DEDICATION

To my parents and sister, Jerry, Sandy and Ferrell, for believing in me,

To Jon “Matthais” Willett for keeping me sane,

To Geoff G. for watching the sky,

To Rebekah for motivating me,

To Larry for reminding me,

To Ming and Geoff for feeding me,

To Francesca for standing by me,

To Dax for being a raughy,

I love you all.

TABLE OF CONTENTS

I. ABSTRACT……………………………………………………………….... 8

II. INTRODUCTION………………………………………………………….10

1.1 Project Background ………………………………………………………..10

1.1.1 An introduction to caves…………………………………………10

1.1.2 An introduction to cave ecology…………………………………11

1.1.3 Sources of energy in caves………………………………………12

1.1.4 Microbial communities in caves…………………………………15

1.1.5 Fungal records from caves………………………………………17

1.1.6 Fungal growth on mineral substrates……………………………20

1.1.7 Assessing fungal communities…………………………………...21

1.1.8 Kartchner Caverns………………………………………………23

1.2 Dissertation Format ……………………………………………………….25

III. CURRENT STUDY……………………………………………………….28

2.1 Fungal communities on speleothem surfaces in Kartchner Caverns,

Arizona, USA ……………………………………………………….….…..28

2.2 Assessing fungal community structure from mineral surfaces in

Kartchner Caverns using multiplexed 454 pyrosequencing ………..…..28

TABLE OF CONTENTS (continued)

2.3 Assessing fungal community structure within bat guano from Kartchner

Caverns using multiplexed 454 pyrosequencing ………………………..30

2.4 Perspectives on the current research and its implications ……………..31

IV. REFERENCES …………………………………………………………..34

APPENDIX A: FUNGAL COMMUNITIES ON SPELEOTHEM SURFACES IN

KARTCHNER CAVERNS, ARIZONA, USA………………………………………...43

APPENDIX B: ASSESSING FUNGAL COMMUNITY STRUCTURE FROM

MINERAL SURFACES IN KARTCHNER CAVERNS USING MULTIPLEXED 454

PYROSEQUENCING ………………………………………………………………..58

APPENDIX C: ASSESSING FUNGAL COMMUNITY STRUCTURE WITHIN BAT

GUANO FROM KARTCHNER CAVERNS USING MULTIPLEXED 454

PYROSEQUENCING …………………………………………………………..…..113

I. ABSTRACT

Research regarding the distribution and structure of fungal communities in caves is lacking. The current study examines fungal communities in Kartchner Caverns, a mineralogically diverse cave located in the Whetstone Mountains, Arizona, USA. The first study examines culturable fungal diversity from speleothem surfaces. Twenty-one fungal genera represented by 43 genotypes and 53 distinct morphological taxonomic units (MTU) were recovered from 15 speleothems. Analysis of DGGE profiles indicated a significant effect of sampling site on community structure. The second study examined fungal diversity from speleothem and rock wall surfaces using the 454 FLX Titanium sequencing platform using the rDNA internal transcribed spacer 1 (ITS1) as a genetic marker. Fungal diversity was estimated and compared between speleothem and rock wall surfaces and its variation with distance from the natural entrance of the cave was quantified. Effects of environmental factors and nutrient concentrations in speleothem drip water at different sample sites on fungal diversity were also examined. Sequencing revealed 2219 fungal operational taxonomic units (OTUs) at 95% similarity.

Speleothems supported a higher fungal richness and diversity than rock walls, but community membership and the taxonomic distribution of fungal OTUs did not differ significantly. OTU richness and diversity were negatively correlated with distance from the natural cave entrance. Community membership and taxonomic distribution of fungal

OTUs differed significantly between the front and back of the cave. There was no observed effect of drip water nutrient concentration on fungal community structure. The third study examined fungal community structure from bat guano over the course of a

9 year. There was no significant difference in fungal OTU richness, diversity, or community membership and taxonomic affiliations among sampling times. There were no significant differences in nutrient concentrations of guano samples among sampling times. Nutrient concentration did have a significant effect on community structure, especially the level of nitrogen and calcium.

II. INTRODUCTION

1.1 Project Background

1.1.1 An introduction to caves

A cave is defined as a void in the ground large enough to allow human entry, generally containing an area devoid of light (Culver and Pipan, 2009). For humans, caves are more than simple holes in the ground. Caves represent sources of mystery, refuge, or even fear across human societies. Throughout history, they have attracted a wide range of people from those seeking a link to the divine to explorers anxious to probe the depths of the earth. Caves have also served as repositories for some of the earliest evidence of modern human behavior, shedding light on the origins of culture (Tollefson,

2012). These unique environments have drawn the attention of generations of biologists as well, for reasons beyond the cultural and anthropological significance of caves.

Initially attracted to the unique morphologies of many troglobitic species, characterized by lack of pigmentation, reduced or absent eyes and elongated morphologies, speleologists soon came to recognize caves as valuable ecological laboratories (Poulson and White, 1969; Culver and Pipan, 2009). Unlike most surface, or epigean, environments, caves represent an ecosystem limited not by nutrients such as nitrogen or phosphorus, but by carbon. Caves also offer ecologists an opportunity to study the organismal interactions of entire communities, a feat made possible due to the reduced number of community members in some cave systems (Poulson and White, 1969; Lavoie et al., 2007). It has also been recognized that up to 15% of the earth’s surface is 11 composed of karst terrain, or land underlain by soluble rock that can be riddled with caves and springs (Culver and Pipan, 2009). As much as 94% of Earth’s unfrozen, fresh water is underground, much of it in these systems (Heath, 1982). Given the importance of fresh water to human activity, scientists strive to understand how these subterranean systems are formed, how their ecosystems are sustained, and how human activity impacts these systems.

1.1.2 An introduction to cave ecology

Caves are typically thought to be very stable, rather homogeneous environments

(Poulson and White, 1969). Relative to surface conditions, caves experience very little fluctuation in temperature. Similarly, many caves experience a fairly constant and high humidity. These characteristics make caves ideal environments for organisms prone to desiccation. The total absence of light in some portions of caves also disturbs circadian cycles that affect many ecological processes on the surface (Lamprecht and Weber, 1992;

Langecker, 2000). In addition, the complete lack of photosynthetic activity in dark areas of caves leaves organisms dependent on carbon sources originating outside of the cave system (Culver and Pipan, 2009).

Caves can be divided into several biotic zones based on prevailing environmental conditions and the presence and amount of available light (Humphreys, 2000). The area closest to the surface is the entrance zone. This zone is characterized by a higher availability of light, supporting photosynthetic organisms, and daily fluctuations in environmental conditions. Just beyond the entrance, the twilight zone is characterized

12 by low light conditions and reduced temperature fluctuations relative to surface conditions. Twilight zones are often inhabited by photosynthetic organisms adept at using low light conditions, such as lichens and certain algae. The twilight zone extends to the point of total darkness, referred to as the dark zone. The dark zone is further divided into three components. The first portion of the dark zone is termed the transition zone.

This area of a cave experiences a complete lack of light, but still experiences fluctuations in temperature and humidity based on surface, or epigean, conditions. The second portion of the dark zone, the deep cave zone, is delimited by the absence of temperature and humidity fluctuations. Finally, the longest and deepest caves are characterized by having a stale air zone. This is the last division of the dark zone, and is characterized by elevated CO2 levels and reduced oxygen (Howarth and Stone, 1990).

1.1.3 Sources of energy in caves

Cave organisms are almost completely dependent upon carbon sources originating outside of the cave system (Culver and Pipan, 2009). These allochthonous nutrient sources can vary from cave to cave, but their availability is typically the limiting factor for heterotrophic communities. One source of carbon comes from large organic matter, such as wood or leaf material, that washes, blows, or falls into caves. These sources, although important in limited cases, are generally unpredictable and are not major sources of nutrients in caves. Dissolved organic carbon (DOC) has come to be recognized as one of the most important sources of energy in cave systems (Simon et al.,

2003; Simon et al., 2007; Culver and Pipan, 2009). DOC generally enters caves via one

13 of two routes, either by percolation through overlying soil and rock or by flowing water in the form of sinking streams. Whereas larger particulate organic matter can enter the cave system via flowing water, often it is quickly dismantled and converted to DOC or fine particulate organic matter (Simon and Benfield, 2001). The input of larger chunks of organic matter via flowing water appears to be of limited importance in deeper portions of caves. The DOC in flowing water, though more abundant, appears to be less important to the overall cave ecosystem (Culver and Pipan, 2009). Flowing water DOC is limited to specific passages of caves, while the DOC in percolating water is more widely distributed throughout the cave. In some cave passages, the DOC in percolating water is the only carbon source available. It is also speculated that the DOC in flowing water might be more difficult to metabolize by many organisms than the DOC in percolating water (Culver and Pipan, 2009). Finally, the DOC in percolating water has been shown to be critical in fostering the development of microbial biofilms (Simon et al., 2003).

These biofilms, or coatings on cave surfaces composed of microbes and their external polysaccharides, form the base of some invertebrate food webs in caves (Boston, 2004).

Another source of energy that can be quite substantial is the product of the active movement of animals into and out of caves. This movement can be regular, such as the nightly foraging of cave crickets or seasonal presence of bats, or sporadic, such as occasional or accidental visitations from larger mammals like raccoons. In either case, this movement brings resources of epigean origin into the cave system, usually in the form of feces. Moreover, it is not uncommon for animal movement to leave behind corpses and other tissues in addition to fecal matter. In caves that support bat

14 populations, bat guano is a major source of externally derived carbon and a keystone resource for many troglodytic species (Culver, 1982; Culver and Pipan, 2009). In some caves, such as Braken Cave in Texas, guano deposition can be quite significant (Barbour and Davis, 1969). This cave supports a bat population of nearly 20 million bats, which are estimated to deposit nearly 50,000 kg of guano per year. Caves supporting larger bat colonies typically experience enough guano accumulation to support specialized guano communities, or guanobionts (Deharveng and Bedos, 2000, Culver and Pipan, 2009). It is well documented that in some Southeast Asian caves multiple food webs, both non- guano specialized and guanobiont communities, are supported on the same guano piles

(Deharveng and Bedos, 2000).

Portions of the guanocentric food webs in caves are based on invertebrates that feed directly on the guano itself. Other portions of these food webs are, however, dependent on microbial, particularly fungal, communities (Ferreira and Martins, 1999;

Ferreira et al., 2007). The mycoflora of guano are responsible for the mobilization of guano nutrients to higher trophic levels (Fletcher 1975, Dickson and Kirk, 1976; Moulds,

2006; Fierriera et al., 2007). The importance of fungi in these trophic systems is highlighted in a number of studies examining the natural history and diversity of invertebrate guano pile communities (Harris, 1973; Dickson and Kirk, 1976; Fierriera et al. 2007; Moulds, 2006). While invertebrate communities are well-studied on this rich nutrient resource in a number of caves, the underlying microbial communities of guano remain relatively unexamined.

The last source of allochthonous carbon found in some caves can come in the form of plant roots. Plant roots commonly are observed in shallower sections of caves, but often extend deeply into cave passages in regions with low water tables to take advantage of the high humidity (Culver and Pipan, 2009). This source of energy is particularly important in several lava tube cave communities supporting a wide range of organisms (Howarth, 1972; Hoch et al., 1999; Oromi and Martin, 1992).

Although caves do not support photosynthesis in their dark zones, limited primary production can take place via chemoautotrophic microbes. Chemoautotrophy is the process of converting the energy stored in chemical bonds into a biologically useful form

(Engel, 2010). In caves, this typically occurs through the conversion of hydrogen sulfide into sulfuric acid. This oxidation reaction not only provides energy for the microbes to produce adenosine triphosphate (ATP), but the byproduct, sulfuric acid, can further enlarge the cave by dissolving the surrounding limestone. This is well documented in a number of caves, including Pestera Movile in Romania and Lower Kane Cave in the

USA (Engel, 2007, Sarbu et al., 1996). There are other reactions that are employed by chemoautotrophic microbes, but sulfur oxidation is the most commonly reported (Engel,

2007).

1.1.4 Microbial communities in caves

Microbes are organisms that are too small to be observed without the aid of a microscope. This category includes bacteria, archaea, fungi, and nematodes. Microbes are found throughout subterranean habitats, and they are known to cover many of the

16 surfaces in caves (Culver and Pipan, 2009). These microbes range from the most common metabolic generalists to highly exotic chemoautotrophs. Originally thought of as contaminants, it is now recognized that microbes are a critical component of cave ecosystems (Caumartine, 1963; Culver and Pipan, 2009). Microbes help shape subterranean environments by breaking down and liberating nutrients for other trophic levels, contributing to the dissolution or precipitation of minerals, and, in some cases, acting as primary producers of organic matter (Culver, 2005). While their importance in cave ecosystems has long been noted, recent developments in methods used to monitor microbial diversity has helped spark new interest among speleologists in microbial communities (Barton and Jurado, 2007).

Microbes are noted for their ability to break down and recycle organic carbon in cave systems. This is a critical function, providing nutrients for other trophic levels, such as invertebrates (Dickson and Kirk, 1976; Culver, 2005). Many of the earliest microbial surveys in caves, conducted using traditional culture-based methods, found microbial populations between epigean and subterranean habitats to be very similar, but they also noted that subterranean habitats were less species-rich (Dickson and Kirk, 1976; Northup and Lavoie, 2004). The discovery of chemoautotrophs in caves changed speleobiology.

Once examination of the wide range of chemoautotrophic organisms in caves began, scientists began to view caves as valuable representatives of what life might look like in extraterrestrial environments. In addition, some scientists even speculated that the chemoautotrophic communities inside of some caves might reflect the first forms of life

17 on earth. As such, caves have become one of the premier sites in which to study the biology of extreme environments (Barton and Jurado, 2007; Barton et al., 2004)

Many microbial studies have focused on geomicrobiology, the contribution of microbial communities to geologic processes (Barton and Northup, 2006; Jones, 2001).

The most fascinating of these studies examine the role that microbes play in the alteration of secondary mineral deposits, or speleothems, in caves. This is an area of intense interest not only for cave conservation, but for possible commercial exploitation as well

(Barton & Northup, 2007; Muynck et al., 2008; Muynck et al., 2010). Evidence of the modulating effects of microbes on speleothem development has been documented in studies in Lechuguilla Cave in New Mexico and other cave systems (Davis et al., 1990;

Cunningham et al., 1995; Cañaveras et al., 2005). However, most of these studies have focused almost exclusively on the prokaryotic communities (Barton & Northup, 2007;

Jones et al., 2008, Cuezva, 2009).

1.1.5 Fungal records from caves

In epigean environments, many fungi are known for their critical roles as decomposers and plant mutualists, and for their vast metabolic capability. Fungi have been referred to as the ultimate generalists for their ability to utilize such a vast array of carbon sources. Given their adaptability to a wide array of nutrient sources and the strongly saprotrophic life styles of many fungi, it is surprising that they have remained relatively unexamined in cave environments.

Studies focused on fungi in caves have frequently addressed concerns about human health (Ajello et al., 1960; Lyon et al., 2004; Jurado et al., 2010). Caves supporting bat populations are often cited as reservoirs for fungal pathogens such as

Histoplasma capsulatum, the causative agent of histoplasmosis (Sacks et al., 1986;

Taylor et al., 1999, Cano and Hajjeh, 2001). Such studies have explored many aspects of H. capsulatum epidemiology, from its distribution in caves to its genetic diversity.

Other studies have examined airborne fungal spore loads in caves (Docampo et al., 2009;

Docampo et al., 2011). These examined the concentrations of Aspergillus and

Penicillium spp., which are known to act as opportunistic pathogens in immunocompromised humans, to produce potent mycotoxins, and to have allergenic properties (Docampo et al., 2009; Docampo et al., 2011).

Cave conservationists have also started to pay more attention to fungal communities in caves. Their studies have addressed concerns ranging from the preservation of the exhibition caves themselves to the preservation of human cultural products in caves (Stomeo et al., 2009; Jurado et al., 2008; Jurado et al., 2010; Martin-

Sanchez et al., 2012; Bastian and Alabouvette, 2009). Shapiro and Pringle (2010) explored the effect of anthropogenic activity on cave ecology by examining the distribution of fungi in caves experiencing varying amounts of human visitation.

Interestingly, they found that caves experiencing minimal human visitation had the highest fungal diversity and those which had never been visited showed no detectable fungi. Other researchers interested in documenting and conserving endemic arthropod communities have recognized that fungi constitute an essential food source for many of

19 these invertebrate communities (Harris, 1973; Dickson and Kirk, 1976). In their 1976 study, Dickson and Kirk showed that arthropod richness and fungal richness in cave substrates was strongly correlated positively. One of the most publicized events related to cave mycology has been the recent fungal colonization of the Neolithic wall paintings in the caves of Lascaux, France. This outbreak has led to a number of publications detailing the identification, source, and mode of colonization for this destructive fungus

(Dupont et al., 2007; Bastian & Alabouvette, 2009; Bastian et al., 2010; Martin-Sanchez et al., 2012).

Finally, other studies have been conducted to examine the mycological biodiversity of cave environments. Koilraj et al. (1999) investigated the distribution of fungi from caves in India. Nováková (2009) has published some of the most extensive inventories of cave fungi . She provides details of fungal diversity on substrates ranging from cave sediments, worm castings, and cave formations from the Domica cave system in Slovakia (Nováková, 2009). Interestingly, few surveys have examined mineral substrates for the presence of fungi. Some notable exceptions are the efforts of Camassa and Febroriello (2003), who explore the role of fungi in the formation of vermiculations in caves, and Went (1969), whose work proposed a role for fungi in the formation of speleothems. Studies focused on guano as a substrate have lead to a number of novel fungal records (Orpurthe, 1964; Nieves-Rivera et al., 2009; Nováková , 2009; Nováková and Kolařík, 2009, Tsuneda et al., 2011).

Caves have also yielded a number of interesting fungi adapted to specialized environments. Kuzmina et al. (2012) isolated and characterized a number of

20 psychrotolerant fungi from Kinderlinskaya Cave in Russia, which were dominated by

Geomyces pannorum. Hsu and Agoramoorthy conducted a study to examine the distribution of thermophilic fungi in caves and the surrounding forest soil (2001). Not only did they find that the thermophiles communities from these two environments were dominated by different genera, they also reported lower fungal diversity in the twilight and dark zones of the caves they examined. Grishkan and colleagues (2004) report on the presence of halotolerant cave fungi in Israel’s Arubotaim Cave. They note the presence of ubiquitous, heavily melanized fungi, such as Alternaria and Aspergillus spp., in this cave system. There are also reports of alkalophilic fungi from limestone caves in Japan

(Nagai et al., 1998).

Recently, public awareness of cave mycology has increased with the emergence of white nose syndrome (WNS). WNS, caused by the fungus Geomyces destructans, is a fungal disease often fatal to bats. It was first observed in the winter of 2006-2007 in New

York (Blehert et al., 2009; Lorch et al., 2011). Since initial reports were made, the disease has spread throughout eastern North America, killing of tens of thousands of bats.

The rapid spread of this disease, and the need to control it, have highlighted the need to better understand the diversity and distribution of fungi in cave systems (Blehert et al.,

2009; Gargas et al., 2009; Lindler et al., 2010).

1.1.6 Fungal growth on mineral substrates

Aside from their better-known ecological roles in soil communities, fungi also are capable of growing directly on mineral surfaces, altering them and establishing

21 microenvironments for other microbes (Burford et al., 2003a; Gorbushina, 2007).

Biological weathering of mineral substrates is accomplished through direct physical separation of particles and the activity of excreted secondary metabolites/ organic acids

(Hoppert et al., 2004). These metabolites alter the pH of the microenvironment occupied by the fungus, which dissolves the rock matrix at its surface (Ehrlich, 1998; Gadd &

Sayer, 2000). In the process of rock dissolution, the fungi mobilize nutrients and provide suitable physical environments for other microbes that inhabit mineral substrates (Gadd

& Sayer, 2000).

Lithic fungi have also been observed to be capable of secondary mineral formation. While exploring the role of fungi in the transformation of carbonate minerals,

Burford et al. (2003) showed that fungi inhabiting their samples precipitated carbonate minerals on their hyphal surfaces. Indeed, some fungi are known to precipitate a variety of minerals on their hyphal surfaces through the adhesion of cations such as Fe, Ni, Zn,

Ag, Cu, Cd, and Pb to their cell walls. These serve as nucleation points for mineral complexes (Gadd, 1990; Sterflinger, 2000). The roles that epilithic and endolithic fungi play in biospeleogenesis are unknown. Considering their ability to transform mineral surfaces, exploration of this community might prove quite fruitful. Given the interest in how microbial communities can affect mineral precipitation in caves, it is interesting that fungal communities in karst systems are not studied more intensely (Gadd, 2004; Engel,

2007).

1.1.7 Assessing fungal communities

Estimates of total fungal diversity on Earth range from 1.5 to 5.1 million fungal species, of which only a small portion has been described (Hawksworth, 2001;

Blackwell, 2011). Fungal community structure has been examined in environments ranging from mineral surfaces to the interior of living plants (Gleeson et al., 2005;

U’Ren, 2012). Initially, studies of fungal community structure utilized traditional culture based methods (Blackwell, 2011). These methods involve the isolation of individual fungal specimens in pure culture (Jumpponen and Johnson, 2005). These survey methods have the advantage of providing individual specimens that can be manipulated and subjected to in-depth phylogenetic scrutiny and taxonomic description. However, it is recognized that these approaches suffer from an inability to capture fastidious or culture recalcitrant fungal species. Recent advances in high-volume sequencing technology, such as 454 pyrosequencing, have redefined the ways in which ecologists can observe and monitor fungal community structure (Blackwell, 2011). These technologies, using phylogenetically informative genetic loci created from the pooled DNA of entire fungal communities, allow for the sequencing of millions of DNA fragments to estimate community structure (Metzker, 2009).

A common finding among all of these environmental sequencing projects is the existence of a hidden rare biosphere that might constitute a large proportion of earth’s biodiversity (Sogin et al., 2006, Buee et al., 2009). In some cases, especially in projects conducted early in the development of these technologies, the number of new unique rDNA sequences interfered with the ability of these studies to analyze fungal community structure (Jumpponen and Johnson, 2005). Since then, new bioinformatic tools and

23 interconnected, sequential data analysis protocols, or pipelines, have been developed, allowing current studies to overcome the initial data overload (Schloss et al., 2009). As environmental sequencing projects using the new platforms became more common, the true extent of the rare biosphere was questioned (Reeder and Knight, 2009). It was shown that the use of certain analysis pipelines could generate artificially high operational taxonomic unit (OTU) richness in human-constructed communities due to systematic sequencing errors (Kunin et al., 2010). Since this discovery, methods have been developed to account for this error and better estimate OTU richness and diversity

(Huse et al., 2010). Some studies have highlighted the differences in observed community composition when comparing the results from culture-based and culture independent surveys, which can differ dramatically in terms of observed species abundances (Aminin, 2008). Considering the contrasting strengths and weaknesses of both inventory approaches, some have concluded that the best estimates of fungal community diversity, structure and function should use both culture-based and culture- independent methods to construct a full picture of microbial diversity (Hugenholtz and

Pace, 1996; Nagy et al., 2011).

1.1.8 Kartchner Caverns

Kartchner Caverns, located in Benson, Arizona, USA (N31° 50’ 08”, W110° 20’

37”), is a wet, actively forming carbonate cave in the Whetstone Mountains. After its discovery in 1974, the cave was kept a closely guarded secret to preserve it from anthropogenic harm. In 1988, the State of Arizona purchased the land surrounding the

24 cave and began development of Kartchner Caverns State Park (Tufts & Tenen, 1999).

Special effort was made during the development process to preserve the pristine status of the cave while making portions of it accessible to tourism. In the process of sustainably developing the cave, in-depth studies were conducted to unravel its hydrology (Garf,

1999), mineralogy (Hill, 1999), microclimates (Buecher, 1999), biology (Buecher &

Sidner, 1999; Welbourn, 1999), and yearly environmental cycles. As a result, Kartchner

Caverns is among the most comprehensively studied carbonate caves in the world.

Kartchner Caverns is noted as one of the top ten caves in the world with regards to mineralogical diversity (Hill & Forti, 1997). Its mineral diversity, protected status, well-studied environmental conditions, and its abundance of carbonate formations make this cave an excellent site to explore microbial communities in a subterranean environment. Consequently, in 2006, Kartchner Caverns was added to the National

Science Foundation’s list of Microbial Observatories (MO). The goal of the Kartchner

Caverns MO is to characterize microbial communities living within the caverns in order to better understand (1) how life exists in this light-deprived oligotrophic environment with respect to community construction, and (2) how these microbial communities might influence speleothem development.

The cave is composed of more than 3 km of passageways, and it is split into two cave complexes, the Big Room and the Rotunda/Throne Room (Hill, 1999). The Big

Room complex, which houses the current natural entrance, hosts a seasonal population of cave myotis bats, Myotis velifer (Buecher and Sidner, 1999). The presence of the M. velifer colony is seasonal, arriving in April to give birth and departing in September. The

25 bat colony is composed of ca. 1,000 - 2000 individuals (Buecher and Sidner, 1999). The bats occupy one section of the cave, the Big Room Complex, during their seasonal visitation. While the bats are present, cave tours for the Big Room complex are not conducted, and visitation is highly restricted.

Since being opened to the public, the cave has continued to be the subject of a number of biological studies. Among these, microbiological investigations were undertaken utilizing both culture and non-culture based methods (Ikner et al., 2007;

Legatzki et al, 2011; Vaughan et al., 2011; Legatzki et al, 2012). These studies have focused largely on mineral surface communities in the cave. Given the importance of bat guano in many cave ecosystems, a microbiological characterization of these guano piles would provide critical information for the further conservation of the Kartchner cave system.

1.2 Dissertation Format

The current study consists of three studies that were conducted to further our understanding of fungal community diversity and structure in Kartchner Caverns. Each study is presented as an independent paper for publication as Appendices A, B, and C.

Each paper in this dissertation has either been published or is under preparation for submission for publication. Due to this format, some overlap exists among the chapters in terms of background information.

Appendix A is an evaluation of fungal community diversity from speleothem surfaces in Kartchner caverns. This study uses both traditional culture based isolation

26 methods and a culture independent community profiling method, denaturing gradient gel electrophoresis (DGGE). Fungal richness was assessed across fifteen calcium carbonate formations based upon recovered culturable fungi. Cultured specimens were identified based on morphological characteristic of the sporulating apparatus and molecular identification based on DNA sequences of the rDNA internal transcribed spacer (ITS).

Similarity of fungal communities among sampling sites was assessed based upon DGGE analysis of 18S rRNA gene amplicons from community DNA.

Appendix B represents a further investigation of fungal community structure from mineral surfaces in Kartchner caverns. In this study, fungal diversity from speleothem and rock wall surfaces was examined using 454 FLX Titanium sequencing of internal transcribed spacer 1 (ITS1) rDNA. This study examines fungal diversity from speleothem and rock wall surfaces and examines variation in fungal communities across a gradient of distance from the natural entrance of the cave. In addition, the effects of environmental factors on fungal diversity were examined, including nutrient concentrations in mineral surface drip water at different sample sites. It was hypothesized that speleothems would exhibit a higher fungal richness and diversity compared to rock walls and support a unique suit of fungi. In addition, it was expected that samples taken closer to the natural entrance would be higher in OTU richness and diversity.

Appendix C examines fungal communities from bat guano in Kartchner Caverns.

The study was designed to track fungal diversity and community structure through the course of a year, timed with the cave’s seasonal bat population. Investigations were

27 conducted using 454 pyrosequencing of community generated ITS1 sequences. It was hypothesized that fungal richness and diversity would decrease in relation to increasing time post bat presence in Kartchner Caverns as the guano piles decompose. Taxonomic composition across sampling times was expected to differ, reflecting observations of fungal succession on dung in literature. Information regarding guano pile nutrient content at each sampling time was collected to assess its effects on fungal community composition. It was further hypothesized that changes in nutrient concentrations found in bat guano among piles would be correlated with shifts in fungal community membership and structure.

III. CURRENT STUDY

The following is a summary of the major findings from each study. The methods, results, and conclusions from this research are presented in the appended manuscripts.

This section concludes with a brief discussion of the research presented.

2.1 Fungal communities on speleothem surfaces in Kartchner Caverns, Arizona,

USA

Appendix A of this dissertation examines the culturable fungal diversity of speleothem surfaces in Kartchner Caverns. In addition, this study examines fungal community heterogeneity using non-culture based community profiling methods

(DGGE). The culturing effort from 15 individual speleothems resulted in the recovery of

21 fungal genera represented by 53 distinct morphological taxonomic units (MTU) and

43 genetic taxonomic units (GTU) based on 100 percent sequence identity. Fungal recovery was much greater than was expected from preliminary mycological work in

Kartchner caverns. The most commonly isolated fungi belonged to the genera

Penicillium, Paecilomyces, Phialophora, and Aspergillus. Analysis of DGGE profiles indicated a significant effect of sampling site on community structure. This study did not address which factors might be responsible for the observed differences in community profiles.

2.2 Assessing fungal community structure from mineral surfaces in Kartchner

Caverns using multiplexed 454 pyrosequencing

Appendix B details the examination of fungal community structure and diversity from speleothem and rock wall surfaces in Kartchner Caverns. This study was conducted using analyses of operational taxonomic units (OTU) based on internal transcribed spacer

1 sequences generated by 454 pyrosequencing. It was found that speleothem surfaces were significantly more OTU rich and diverse than rock wall samples, but they did not differ significantly in regards to community membership or taxonomic affiliation. These results suggest that speleothem and rock wall surface communities share a core suite of fungi. The results also suggest that speleothem communities do support a number of species unique to this substrate. The origin and reasons for maintenance of these taxa are unknown. This study also found that as distance from the natural entrance of the cave increases, fungal community richness and diversity significantly decrease. Similarly, significant differences in community membership and taxonomic composition were observed between samples sites closest to and furthest from the natural entrance. These differences could reflect the importance of the natural entrance as an entry point for fungi in the cave. No significant effects of drip water nutrient concentration on fungal community composition or structure were observed in this study. This could be due to a lack of replication among sampling sites or time elapsed between community and water sampling. Sampling site had a significant effect on community structure. The complex interactions underlying the differences among sampling sites that might account for these differences are not discernable from this study and merit further research.

2.3 Assessing fungal community structure within bat guano from Kartchner

Caverns using multiplexed 454 pyrosequencing

Appendix C presents the findings for an investigation of fungal communities from bat guano. This study first addressed changes in fungal community structure from bat guano in relation to the seasonal absence of bats in Kartchner Caverns employing the same sequencing techniques utilized in appendix B. The study concluded that there was no significant difference in fungal OTU richness, diversity, or community membership and taxonomic affiliations among sampling times. Similarly, there were no significant differences among sampling times in regards to nutrient concentrations of guano samples.

However, nutrient concentration was found to have a significant effect on the structuring of fungal communities, especially the level of nitrogen and calcium. Upon closer inspection of guano piles, it was noted that two of the five piles did not serves as adequate replicates for this study due to differences in pile accumulation during the bat season. These older piles were significantly different in terms of nutrient profile and the fungal communities they supported. When they were removed form the analysis, nutrient concentration was no longer shown to have a significant effect on fungal community structure. The results from this study show that the relative activity of guano piles has a significant effect on fungal community structure from guano. More research is needed to disentangle pile activity from other environmental factors closely correlated to this variable. In addition, this project highlights the need for increased replication, temporally and spatially, to further characterize bat guano fungal communities.

2.4 Perspectives on the current research and its implications

In the process of performing this research, I have gained significant field and laboratory experience related to cave mycology. While the studies presented in this dissertation addressed a number of valuable questions about fungi in caves, there remains a substantial body of work that still needs to be conducted to understand more fully the mycoflora of subterranean environments.

These questions can be summarized by the following topics: fungal mineral surface interactions, sources of cave fungal populations, and community interactions.

Caves are defined by the stone in which they are formed. Biological activity in caves has to occur either on or in mineral substrates. It is critical to characterize more fully the interactions of fungal systems with the mineral substrates in caves to understand their function in this environment. This study demonstrated that fungi, and fungal DNA, were recovered from many mineral surfaces in Kartchner Caverns. I was not able to determine whether theses fungi were actively colonizing the substrate. It is possible that fungal hyphae or resting spores are found dormant on these surfaces. This question could be addressed using electron microscopy of colonized surfaces or the identification of gene activity consistent with fungal growth. The ability of fungi to inhabit and directly affect mineral surfaces is well-documented in epigean habitats. There is no reason to expect that actively growing fungi would not be able to modify similarly mineral surfaces below ground. This ability to both dissolve and precipitate minerals is of special interest to cave conservation in terms of preserving speleothems and the microbes existing on

32 them. Preliminary studies conducted in conjunction with the current project suggest that a number of fungi isolated from the cave are able to precipitate crystals in artificial medium. The continuation of these studies would allow for better characterization of these crystals and the distribution of this precipitation ability among cave fungi.

The current study provides substantial information regarding fungal diversity and distribution in Kartchner Caverns. It does not provide, however, information regarding the sources of fungi in the cave. Appendix B alludes to the importance of the entrance as a source of fungal diversity. To examine this relationship more closely, sampling should be extended to the entrance of the cave and the surrounding epigean environment. Nor did this study address the impacts of human activity on the mycoflora, although attempts were made to exclude anthropogenic influences. Sampling conducted between sites of varying human impact could provide essential information regarding anthropogenic influences. This study addresses the spatial distribution of fungal communities through a large portion of the cave, but does not explore how fungi might spread in the cave. In- depth sampling of aerospora, mapping air movements and flooding events, and monitoring temporal shifts in community structure would provide invaluable insights into mechanisms if subterranean fungal movement.

Finally, it is important to recognize that the fungal community has to interact and compete with a wide range of other organisms in the cave. There is increasing interest in the complex interdependence among organisms representing all portions of the tree of life. The limited size of speleothem microbial communities makes them idea subjects for investigations of inter-kingdom interactions. The ability of many of the fungi and bacteria

33 isolated during the course of this study to produce antimicrobial compounds was evidence that these interactions were occurring. Beyond interactions limited to microbes, the relationship between guano pile invertebrate communities and fungi would be another interesting relationship to study, especially in light of the importance of fungi in invertebrate food webs. The current study provides valuable information on the structure of one component of cave ecology that can be used to help inform future investigations of the broader system.

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APPENDIX A

FUNGAL COMMUNITIES ON SPELEOTHEM SURFACES IN KARTCHNER

CAVERNS, ARIZONA, USA

Copyrighted by Vaughan et al. 2011

Published in International Journal of Speleology 40(1): 65-77

doi: 10.5038/1827-806X.40.1.8

This is an open-access article distributed under the terms of the Creative Commons

Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

APPENDIX B

ASSESSING FUNGAL COMMUNITY STRUCTURE FROM MINERAL SURFACES

IN KARTCHNER CAVERNS USING MULTIPLEXED 454 PYROSEQUENCING

In preparation for submission to Microbial Ecology

Title: Assessing fungal community structure from mineral surfaces in Kartchner Caverns

using multiplexed 454 pyrosequencing

Michael Joe Vaughan, Will Nelson, Cari Soderlund, Raina M. Maier, and Barry M. Pryor

Abstract

Research regarding the distribution and structure of fungal communities in caves is lacking. Kartchner Caverns is a wet and mineralogically diverse cave located in an escarpment of Mississippian Escabrosa limestone in the Whetstone Mountains, Arizona,

USA (N31° 50’ 08”, W110° 20’ 37”). Fungal diversity from speleothem and rock wall surfaces was examined with 454 FLX Titanium sequencing technology using the internal transcribed spacer 1 (ITS1) as a genetic marker. Fungal diversity was estimated and compared between speleothem and rock wall surfaces and its variation with distance from the natural entrance of the cave was quantified. Effects of environmental factors and nutrient concentrations in speleothem drip water at different sample sites on fungal diversity were also examined. Sequencing revealed 2219 fungal OTUs at the 95% similarity level. Speleothems supported a higher fungal richness and diversity than rock walls. However, community membership and the taxonomic distribution of fungal OTUs at the class level did not differ significantly between speleothems and rock walls. Both

OTU richness and diversity decreased significantly with increasing distance from the natural cave entrance. Community membership and taxonomic distribution of fungal

OTUs also differed significantly between the sampling sites closest to the entrance and

60 those furthest away. Finally, there was no observed effect of drip water nutrient concentration on fungal community structure. Together, these results suggest that proximity to the natural entrance is an important factor in determining fungal community structure from mineral surfaces in Kartchner Caverns.

Introduction

Caves and the organisms that inhabit them have been objects of fascination and inquiry for many researchers. These unique environments provide an opportunity to study organisms adapted to an extreme environment of darkness and oligotrophy.

Recently, microbial communities that exist inside of caves have attracted increasing interest (Barton and Jurado, 2007). The genesis of these studies is grounded in questions concerning diverse interests such as geomicrobiology (Barton and Northup, 2006; Jones,

2001), human health (Bhullar et al., 2012; Jurado et al., 2010), the biology of extreme environments (Barton and Jurado, 2007; Barton et al., 2004), and art conservation

(Schabereiter-Gurtner et al., 2004; Bastian et al., 2010). Most of these studies have focused on bacterial communities, without attention to biology and ecology of other microbial groups such as fungi.

Studies focused on fungi in caves generally concern human health. Caves, especially those supporting bat populations, are often cited as reservoirs for fungal pathogens such as Histoplasma capsulatum, causative agent of histoplasmosis (Sacks et al., 1986; Taylor et al., 1999, Cano and Hajjeh, 2001). A separate group of studies has investigated airborne spore loads in caves and their possible origins and health effects

(Docampo et al., 2009; Docampo et al., 2011). Other studies have examined the impact of fungi on cave conservation. These studies concern the conservation of show caves themselves and the preservation of cultural products in caves, such as paintings (Jurado et al., 2008; Jurado et al., 2010; Martin-Sanchez et al., 2012; Bastian and Alabouvette,

2009). Went (1969) examined fungi associated with stalactite growth and mineral

62 precipitation. Other studies have examined the biodiversity of fungi associated with cave environments in general (Hsu and Agoramoorthy, 2001; Grishkan et al., 2004). Koilraj et al. (1999) investigated the distribution of fungi from caves in India, and Nováková (2009) created extensive inventories of fungi from the Domica cave system in Slovakia. A study by Shapiro and Pringle (2010) examined the effect of anthropogenic activity on the distribution of fungi in caves. Whereas few studies have quantified fungal diversity in caves, interest in cave mycology has increased since 2007 with the emergence of white nose syndrome (WNS) in the Eastern United States, a bat disease caused by Geomyces destructans, an often fatal fungal disease (Blehert et al., 2009; Gargas et al., 2009; Lorch et al., 2011). Efforts to understand the epidemiology and ecology of this disease have highlighted the need for a better understanding of the dynamics of fungal communities in caves.

With an estimation of between 1.5 and 5.1 million fungal species on earth, only a small portion of fungi have been described (Hawksworth, 2001; Blackwell, 2011). One of the most important advances toward the exploration of microbial diversity has been the advent of high throughput sequencing technologies such as 454 pyrosequencing

(Blackwell, 2011). This technology permits deep sequencing of phylogenetically informative genetic loci created from the pooled DNA of entire fungal communities.

These environmental sequencing projects have revealed a hidden rare biosphere that might constitute a large proportion of Earth’s biodiversity (Sogin et al., 2006). Culture independent methods, such as environmental sequencing, are needed to complete a full

63 picture of microbial diversity (Hugenholtz and Pace, 1996; Reeder and Knight, 2009;

Kunin et al, 2010; Nagy et al., 2011).

Kartchner Caverns, a wet and mineralogically diverse cave located in an escarpment of Mississippian Escabrosa limestone in the Whetstone Mountains (N31° 50’

08”, W110° 20’ 37”). The site is the main attraction for Kartchner Caverns State Park near Benson, Arizona, USA. Since its discovery in 1974 and subsequent purchase and development by the state of Arizona into a highly popular show cave, special effort has been made to preserve the pristine status of much the cave while making portions of it accessible to tourism. In the process of responsibly developing the cave, many baseline studies concerning the physical, environmental and biological qualities of the cave were conducted. After development, environmental monitoring projects and biological surveys have continued. In combination with the parks strong emphasis on conservation, stewardship, and scientific inquiry, these resources make Kartchner Caverns an ideal site to investigate subterranean microbial communities.

In this study, fungal diversity from speleothem and rock wall surfaces in

Kartchner Caverns was examined with 454 FLX Titanium sequencing technology using the internal transcribed spacer 1 (ITS1) of rDNA as a genetic identification marker. This study was designed to estimate and compare fungal diversity from speleothem and rock wall surfaces and examine its variation across a gradient of distance from the natural entrance of the cave. Given the generally constant presence of flowing water on speleothems in the study area, it was hypothesized that the cave formations would support a greater diversity of fungi and a community different from rock walls. It was

64 also hypothesized that fungal richness and diversity would be greater in samples taken in closer proximity to the natural entrance of the cave. In addition, the possible effects of environmental factors and nutrient concentrations in speleothem drip water at different sample sites on fungal diversity were examined. It was expected that variation observed in fungal community structure would be attributable to nutrient concentrations in water from the mineral surfaces of speleothems and rock walls.

Materials and Methods

Sampling Sites

Sampling was conducted along a 450 m long transect through a low impact section of

Kartchner Caverns (receiving less than 3 visitations per year as per Ikner et al., 2006)

(Fig. 1). The transect began ca. 275 m into the cave to avoid passages possibly influenced by bats. Samples consisted of individual speleothems or rock wall sections.

Three speleothems and three adjacent rock wall sections were sampled per sampling site.

All speleothems sampled were stalactites measuring less than 10 cm at the base, of a similar color, and actively dripping (based on direct observation). Sites were selected randomly from all possible sites containing at least three speleothems matching the sampling criteria. All samples within a site were within 3 m of each other. There were a total of eight sampling sites selected, yielding a total of 24 speleothem samples and 24 rock wall samples (Supp. Table 1).

Measurements of environmental conditions

Environmental variables monitored across the transect included CO2 concentration and temperature. Data for these variables were extracted from four- channel external environmental monitoring sensors (Onset Corporation, Bourne, MA) used in routine monitoring of cave conditions and other research projects (Blasch, 2011)

(Fig 1). At each sample site, drip water pH was measured using an Orion micro pH electrode (Thermo Scientific, Beverly, MA) attached to an Accumet portable AP110 pH meter (Fisher, Hampton, NM) by sticking the needle probe directly into the drip water on the speleothem. Drip water was also collected during a sampling trip on February 4th,

2012, by touching absorbent glass microfiber filters to the speleothem and rock wall surfaces at each site for ca. 2 min (GF/A 42.5 mm circles, Whatman div. of GE

Healthcare, Waukesha, WI). Filter disks hold ~600ul of drip water when saturated. Ten disks were used per site, yielding between 2 and 6 ml of drip water for each site. Water samples were stored at -80C until they were submitted to the Arizona Lab for Emerging

Contaminates (ALEC) for nutrient analysis. Total nitrogen (TN) and organic carbon

(TOC) were measured on a TOC analyzer with TN module, model TOC-VCSH

(Shimadzu, Columbia, MD). Anion concentrations including phosphate and nitrate were measured by ion chromatography on a Dionex ICS-1000 using a AS-22 anion exchange column and guard (Dionex, Sunnyvale, CA). Finally, the concentrations of a number of metals, including iron, manganese, potassium and sodium, were measured using an Elan

DRC-inductively coupled plasma-mass spectrometer (PerkinElmer Life and Analytical

Sciences, Shelton, CT).

Fungal community sampling

Sampling of fungal communities was conducted over two days due to the length of time required for the sampling process. Sample sites 1 – 4 were sampled on January

25th 2011, and sites 5 – 8 were collected on February 5th, 2011. Each speleothem was sampled in three 20cm2 bands around the circumference of the formation at its base, middle, and growing tip. Preliminary studies demonstrated that this sampling technique yielded representative samples of an entire speleothems (data not shown). Rock wall samples adjacent to each formation were similarly sampled in three 20cm2 patches. Each

67 of these three subsamples was kept separate and processed individually. Speleothem and rock wall samples were taken using the modified swab method as described in Vaughan et al. (2011) with the alteration that five sterile cotton swabs were used per sub-sample.

All samples were placed on ice and transported to the laboratory where sample were homogenized by vortexing in sample tubes for one min before removing cotton swabs.

Community DNA was then extracted from the remaining suspension.

DNA extraction, amplicon preparation, and sequencing

The suspension from each swab was centrifuged in 1 mL aliquots at 14,000 x g for 10 min at 4°C in 1.5 ml microcentrifuge tubes. DNA was extracted from the resulting pellet using the FastDNA Spinkit for Soil (MP Biomedicals, LLC, Solon, OH) following standard manufacturer protocol with the modification that silica binding matrix was left to dry overnight in the spin filters after washing with the proprietary SEWS solution before DNA elution.

DNA extracts were inspected visually for DNA quality by transillumination in an

EpiChem3 imaging system (UVP, San Gabriel, CA) following gel electrophoresis and staining with ethidium bromide. Extracts with high weight DNA bands and little smearing were then quantified by DNA fluorescence using Quant-it PicoGreen dsDNA stain (Molecular Probes Inc, Eugen, OR) and a Synergy H1Hybrid multi-mode microplate Reader (BioTek, Winooski, VT) reading the fluorescence wavelengths 480 nm (excitation) and 520 nm (emission) as per manufacturer instructions. After quantification, DNA solutions were brought to a standard concentration of 1 ng/ul for polymerase chain reactions (PCR).

Three independent PCR reactions were performed on each DNA extract from each sub-sample. The primers used in these reactions targeted the fungal internal transcribed spacer 1 (ITS1) rDNA region using a forward fusion primer designed on

ITS1F (CTTGGTCATTTAGAGGAAGTAA) and the reverse primer ITS2

(GCTGCGTTCTTCATCGATGC) (White et al., 1990). The ITS region of fungal rDNA has been shown to be a useful genetic locus for fungal identification and environmental sequencing (Nilsson et al., 2009; Schoch et al., 2012). The forward fusion primers contained the 25 bp pyrosequencing primer A (PPA), a 10 bp sample specific sequence tag (ST), and the primer ITS1F (5’- (PPA) CGTATCGCCTCCCTCGCGCCATCGA–

(ST) NNNNNNNNNN– (ITS1F) CTTGGTCATTTAGAGGAAGTAA -3’). A total of

48 different fusion primers were designed for this study, one for each speleothem and rock wall sampled (Supp. Table 2). Each 25 uL PCR reaction contained 0.08 uM of each primer, 0.2 mM of each dNTP, 1X buffer (containing 10 mM Tris-HCl (pH 8.8), 50 mM

KCl, and 0.08% Nonidet P40), 2.5 mM MgCl, 1 unit of DreamTaq DNA polymerase

(Fermentas Inc., Burlington, Ontario), and 0.08 ng uL-1 environmental DNA. In cases where PCR reactions failed, extracted DNA was subjected to serial dilutions down to a factor of 10-2 to reduce the effect of PCR inhibitors in the extract. After confirming PCR success using gel electrophoresis and transillumination as described previously, individual reactions were subjected to a PCR clean up using the QIAquick PCR purification kit (Qiagen Inc., Valencia, CA) as per manufacturer instructions. After reaction cleanup, each PCR reaction was quantified by DNA fluorescence using Quant-it

PicoGreen dsDNA stain as described above. After quantification, each of the three

69 individual PCR reactions for each subsample were combined to achieve equal molar concentrations of amplicons. Each pooled subsample was then combined for each sample to achieve equal molar concentrations of amplicons. Individual subsamples were subjected to separate extractions, PCRs, and pooling to help reduce the effect of individual reaction PCR bias. These representative amplicon pools for each of the 48 samples were then brought to a DNA concentration of 1x109 DNA molecules per ul for submission to the Arizona Genomics Institute for sequencing on the Titanium GS FLX

454 platform. Multiplex sequencing was conducted on two separate plates. Samples were randomly assigned to sequencing pools.

Sequence processing and quality control

Initial processing of sequences was conducted using mothur v. 1.21.0 (Schloss et al., 2009). Sequences were removed if they had any ambiguous bases, had quality scores lower than 30, were shorter than 150 bp or longer than 400 bp, and if they did not contain the entire primer and barcode sequence. The filtered data set was then dereplicated by removing sequences of 100 % sequence similarity. Sequences were then subjected to a pseudo-single linkage clustering algorithm using the mothur pre.cluster command using default parameters to help remove sequences generated due to sequencing errors and reduce the size of the data set (Huse et al., 2010; U’Ren, personal comm.). The pre- cluster sequences were then aligned in the program ESPRIT using an average linkage

Needleman-Wunsch pairwise alignment (Sun et al., 2009). The resulting distance matrix was used to cluster sequences into OTUs in mothur at 100, 97, 95 and 90 percent sequence similarity. The most abundant sequence per OTU was chosen as the

70 representative sequence for each OTU. These representatives were then scanned for the presence of chimeric sequences using the Alaskan Fungal Metagenomics Project’s pipeline chimera check tool under default settings (http://www.borealfungi.uaf.edu/).

Flagged sequences were manually checked by quarrying separate portions of the sequences against the Alaskan Fungal Metagenomics Project’s annotated ITS database.

Sequences subjected to queries, using the basic local alignment search tool (BLAST), that resulted in parent sequences from different organisms were eliminated from the data set.

Finally, the non-chimeric OTU representatives were processed using an ITS1/ITS2 extractor for fungal ITS sequences (Nilsson et al., 2011). Sequences corresponding to loci other than ITS1 were removed from the data set.

Fungal community assessments

For all analyses, fungal OTU sequences at the 95% similarity level are reported unless otherwise noted. To examine sampling efficiency and completeness, the software package EstimateS v. 8.2 was used to compute species accumulation curves and estimated true species richness for speleothem and wall samples (Colwell, 2005).

Calculations were made using 50 randomizations of the observed data. Sampling was considered complete when the bootstrap species richness estimator entered the 95% confidence intervals for the observed species accumulation. When observed species accumulation curves did not lie within the 95% confidence interval for the estimated true species richness, subsequent analyses were conducted with a dataset excluding rare OTUs

(n < 10) unless noted (Sogin et al., 2006).

Basic alpha diversity statistics were calculated as observed species richness and

Fisher’s alpha () for each speleothem and wall sample and for each sample site using the software package PAST v 2.01 (Hammer et al., 2001). Differences in species richness and diversity were examined using MYSTAT v 12.02 (SYSTAT Software Inc.,

Chicago, IL) using a pairwise t-test of speleothem samples versus adjacent wall samples.

The full dataset with rare taxa included was used in these calculations.

To examine the similarity of fungal communities from speleothems and rock walls, a cluster analysis was conducted based on the Jaccard similarity index (defined as the number of shared OTUs between two samples divided by the total number of OTUs in both samples). If rock walls supported fungal assemblages distinct from those of speleothems, one would expect to see two different clusters, each composed of one substrate type. In addition, samples were subjected to ordination using non-metric multidimensional scaling (NMDS). To test a null hypothesis of no difference between the observed speleothem and rock wall communities an analysis of similarity (ANOSIM) was then conducted on the dataset using the Jaccard and Morisita-Horn indices as measures of similarity and 10,000 permutations. For beta diversity analyses, a presence- absence (Jaccard) and an abundance dependant index (Morisita -Horn) were used due to some question concerning the correspondence of read abundance and actual OTU abundance (Amend et al., 2010).

Least squares regression was used to examine the effect of distance along the sampling transect and the distance between individual samples on fungal diversity index and species richness using MYSTAT. After confirming that the data met the

72 requirements for regression analysis, regression was performed for speleothems and rock walls separately and combined. When exploring the effect of distance between sampling sites on community similarity, diversity indices (Jaccard and Morisita-Horn) were square root transformed and distances were square root + 1 transformed to attain normality. The similarity of fungal communities along the transect was also analyzed using hierarchal clustering as described above. The data set was then subjected to NMDS and ANOSIM to further investigate differences among samples along the transect.

The filtered data set of fungal OTU sequences was also subjected to BLAST searches against the Alaskan Fungal Metagenomics Project’s annotated ITS database to place the OTUs to a subphylum and class level identification. For each OTU, the top five

BLAST hits were examined. All BLAST hits with bit scores less than 100 were excluded from consideration and given the status of “poor BLAST match”. The alignments for sequences with bit scores between 101 and 200 were examined by eye and given the status of either “poor BLAST match” or their associated taxonomic description. In cases where all five BLAST hits disagreed, a consensus designation was made among all hits.

In cases where the top five BLAST hits matched unidentified fungal voucher sequences, the isolates were given the status of unidentified. The null hypothesis that the distribution of members among represented subphyla was the same in speleothem and rock wall samples was tested using the Chi2 test statistic on percent normalized data for two sampled datasets using the software PAST (U’Ren et al., 2010). The same test was conducted using class level designations for OTUs from speleothems and rock walls and

73 for the subphylum and class level designations for OTUs sampled from the four sampling sites closest to the cave entrance compared to the four furthest from the entrance.

Analysis of environmental conditions and effects on fungal community structure

The nutrient content of drip water from the four sites where speleothem and rock wall water was collected were compared using principal component analysis (PCA) in

PAST. All nutrient data were log transformed before analysis. To examine the effects of environmental variables across the transect, least squares regression (MYSTAT) was used to explore changes in diversity index due to measured drip water nutrient content.

These analyses used diversity statistics as computed above, but combined samples on site, yielding 8 speleothem and wall samples. The relationship of environmental factors to community composition was then analyzed using canonical correspondence analysis

(CCA) in PAST. CCA is a direct gradient analysis used to test hypotheses concerning environmental factors and observed community structure (Legendre and Legendre, 1998;

Palmer, 1993; ter Braak, 1986). In these analyses, the effect of both categorical variables

(sampling site) and continuous variables (nutrient content of drip water) were tested.

Significance of CCA axes was computed using a permutation analysis (permutation =

10,000).

Results

Sequence processing and quality control

The sequencing effort resulted in 1,022,447 unidirectional reads across 24 speleothem samples and 24 wall samples (Supp. Table 1). Sequences were spread evenly among the samples, with a few notable exceptions. Wall samples 3 and 5 resulted in only

5 and 6 sequences respectively, and wall sites 4, 6, and 7 resulted in no sequence recovery. These samples were in separate sequencing pools and displayed positive PCR products and DNA quantifications. For comparisons requiring paired data, speleothem samples corresponding to rock wall samples 3-7 were excluded. There was no difference in sequence recovery from speleothem and rock wall substrates (ANOVA, F=0.02315,

P=0.8798).

After initial filtering and quality control, a data set of 743,344 sequences were collapsed into 66,248 genotypes (100% sequence similarity) and 2,291 OTUs based on

95% sequence similarity (Supp. Table 1). The sequences from speleothems (total =

540,602) represented 42,810 genotypes and 1,755 OTUs (15.4% were singletons, and

43.0% had abundances <10). Rock wall sequences (n = 438,549) were represented by

29,387 genotypes and 1,327 OTUs (14.8% were singletons, and 39.3% had abundances

<10).

Fungal community assessments

The sampling effort was insufficient to capture the full ITS1 based OTU richness across all sites (Fig. 2a). Similarly, the full OTU richness was not captured for the speleothem or rock wall substrate alone (Fig. 2b, c). This prevented discrimination

75 between truly rare taxa and those that were simply not captured in the sampling effort.

As a result, a dataset excluding rare taxa was used when comparing diversity among samples, sites and substrates.

When all speleothem and all rock walls samples were compaired by rarefaction, limiting the number of speleothem sequences to equal the 297,837 wall sequences sampled during rarefaction, speleothems (985 OTUs, 95% confidence interval (CI) =

955.4 – 1015.6) were more species-rich than wall samples (759 OTUs, 95% CI = 727.2 –

790.8) (Fig. 2d). Speleothems had significantly higher OTU richness compared to adjacent rock wall samples (pairwise t-test, t18 = 3.075, P = 0.007). The same comparison revealed that speleothems also had higher OTU diversity, measured as

Fisher’s alpha, than rock walls (pairwise t-test, t18 = 5.306, P <0.0001).

Differences in fungal communities

Differences in community composition were visualized using non-metric multidimensional scaling (NMDS), where speleothem samples form a cluster overlapped by the rock wall samples (stress = 0.1807) (Fig. 3a). While speleothem fungal communities were more OTU-rich and diverse than rock wall communities, there was no evidence that they differed in species composition (ANOSIM on Jaccard distances: mean within rank = 445.9, mean between rank = 458, R = 0.0269, P = 0.1888). This conclusion was reinforced by a cluster analysis based on Jaccard similarity, which did not resolve distinct speleothem and rock wall clusters (data not shown). Among all speleothem and rock wall samples, ca. 75% of the non-rare OTUs recovered from wall samples were shared with speleothems.

The effect of distance along the sampling transect predicted OTU richness for both speleothems and rock walls (Least squares regression, R2 = 0.553, P < 0.0001; R2, P

= 0.016 respectively, OTU at 0.05 level). As distance increased along the transect from the point closest to the cave entrance, OTU richness decreased (Fig. 4a). Increasing distance along the transect had the same effect on fungal OTU diversity for speleothems and rock walls (Diversity as Fisher’s alpha, Least squares regression, R2 = 0.447, P <

0.0001; R2 = 0.660, P < 0.0001 respectively). Samples taken from sites further from the natural cave entrance had lower fungal OTU diversity (Fig. 4b). Investigations of community similarity between the four sites closest to the natural entrance (front sites) and those distal to the natural entrance (back sites) showed that these two groups differed significantly in community composition (ANOSIM on Jaccard distances: mean within rank = 352.8, mean between rank = 549.3, R = 0.435, P < 0.0001). This difference was visualized using NMDS, which shows speleothem and wall samples from the front overlapping with each other in a separate space from the overlapping speleothem and wall samples from the back section of the transect (Fig. 3b). There was also a significant negative relationship between increasing distance between sampling sites and fungal community membership similarity as measured by the Jaccard and Morisita-Horn indices

(Least squares regression, R2 = 0.13, P < 0.0001; R2 = 0.27, P < 0.0001 respectively)

(Fig. 4c). The same effect was observed for both indices when examining wall samples alone, but not speleothems (Least squares regression, R2 = 0.31, P < 0.0001(Jac.); R2 =

0.26, P < 0.0001(MH); R2 = 0.06, P < 0.0001 (Jac.); and R2 = 0.007, P = 0.169 (MH) respectively).

When comparing the distribution of fungal sub-phyla, there was no significant difference in community composition among speleothem and wall samples (N1 = 96, N2

= 100, df = 7, Chi2 = 4.3105, P = 0.7434) (Fig. 5a). The same was true for class-level identifications (N1 = 95, N2 = 100, df = 15, Chi2 = 12.531, P = 0.6385). However, comparisons of the four sampling sites closest to the natural entrance to those four furthest away revealed significant differences in both sub-phylum (Fig. 5b) and class representation in the observed OTU communities (N1 = 97, N2 = 100, df = 6, Chi2 =

34.369, P < 0.0001; and N1 = 94, N2 = 100, df = 13, Chi2 = 50.524, P < 0.0001 respectively).

Analysis of environmental conditions and effects on fungal community structure

The PCA biplot that was constructed to examine possible differences in drip water nutrient content from speleothems and rock walls among 4 sampling sites accounted for

85.6% of the variance in the data set (First axis eigenvalue = 0.2849, 62.9 % variance,

Second axis eigenvalue = 0.1030, 22.7% variation) (Fig. 6). The biplot lines represent increasing concentration of the indicated nutrient. The first axis was positively correlated with K, Na, and Fe. The second axis was positively correlated with Mn and negatively associated with total organic carbon (TOC) and total nitrogen (TN). The position of the sites and the associated substrate from which the water sample was collected along the biplot nutrient lines indicates the level of that nutrient in that sample as compared to the other samples analyzed. The PCA analysis reveals that though the nutrient content of drip water samples from the speleothems and rock walls was not identical, the differences

78 associated with sampling site was more extreme, indicated by the fact that each sample site occupies its own quadrant of the ordinal space.

No relationship was observed between site and any of the environmental variables measured (Table 1) (data not shown). Similarly, no significant relationships could be found between observed OTU diversity or richness and the environmental variables measured (data not shown), except for distance from the entrance of the cave (Fig. 4).

CCA triplots were constructed to test whether observed environmental variables had a significant effect on observed community composition. The first CCA analysis tested whether the measured drip water nutrient content had an effect on community composition. This test failed to produce significant axes after a permutation test (trace =

2.714, trace P = 0.1584; axis 1 eigenvalue = 0.7538, P = 0.6139; axis 2 eigenvalue =

0.6192, P = 0.8416), indicating no relationship. However, the second triplot that examined the effect of sampling site on community composition resulted in significant axes (trace = 3.219, trace P = 0.0297; axis 1 eigenvalue = 0.7638, P = 0.0099; axis 2 eigenvalue = 0.6229, P = 0.0297), indicating a significant effect of sampling site on community composition (Fig. 7).

Discussion

An investigation was conducted to assess fungal community structure from mineral surfaces in Kartchner Caverns and the effects of environmental variables on the observed communities. Cave formations were hypothesized to support a greater diversity of fungi than rock walls. In addition, the community from speleothems was expected to differ from that of rock walls. This study also tested whether fungal richness and diversity were greater in samples taken in closer proximity to the natural entrance of the cave. It was expected that variation observed in fungal community structure would be attributable to nutrient concentrations in water from the mineral surfaces of speleothems and rock walls.

In regards to the first hypothesis, speleothems in Kartchner Caverns support a larger number, and greater diversity of, fungal OTUs than do rock walls. Interestingly, however, the members of the rock wall fungal community did not differ significantly from those of speleothems. This suggests that rock walls support at least a partial subset of fungal species found on speleothems. However, the fact that speleothems support a greater diversity and richness of fungal OTU suggests that there is something unique about speleothems that allows them to support a larger number of fungal species.

Originally, it was hypothesized that these differences would be explained by differences in nutrient content of surface water from speleothems and rock walls. The results from the PCA of nutrients by site did not support this idea. The PCA indicated that rock wall and speleothem water samples from the same sampling sites had more similar nutrient concentrations than all rock wall or speleothem samples together,

80 suggesting separation of samples according to site, not substrate. A CCA analysis testing the hypothesis that nutrient concentrations would have a significant effect on community membership led us to reject this hypothesis since no significant axes could be produced.

The only environmental variable measured that had a significant effect on community structure was sampling site.

Similar results were obtained in an investigation of the factors affecting speleothem bacterial community structure in Kartchner Caverns (Legatziki et al., 2012).

This study demonstrated that these communities experienced no significant impact of the nutrient composition of the speleothems on which they were growing. Instead, this study found that sampling site of the speleothems, in either one of two rooms, strongly affected bacterial community composition. Specifically, the authors found that the profiles of bacterial communities from one room, though not identical, were more similar to each other than to profiles between rooms. Additionally, this study demonstrated that bacterial community structure was strongly influenced by the drip line, or water source, for each formation. This suggested that some factor in the drip water was significantly affecting bacterial community structure. The authors suggest that these differences in community composition could be due to fluctuations in drip water flow, different nutrients or nutrient concentrations in the drip water, or bacteria transported with drip water that could enable unique bacterial communities to form on individual formations. Although a direct comparison between the current study and Legatziki et al. (2012) is not possible, these studies indicate that distance between sampling sites has an impact on microbial community structure in the cave. We cannot completely discount the possibility that drip

81 water nutrient content has an effect on fungal community structure in the context of this study due to the possibility that the subtle differences that might be present were not observable with this water sampling collection method of pooling samples on site, which might contain multiple drip lines that have been shown to affect bacterial community structure. Future studies aimed at unraveling the relationship between cave drip water and microbial community structure will need to structure sampling so as to examine small room-level and larger cavern-level spatial scales.

Another possible effect of cave drip water on fungal communities could be that it carries fungal propagules into the cave system while percolating through the overlying soil/rock matrix. Legatzki et al. (2012) hypothesized that this might be a possible effect of drip water on bacterial community structure. The present study suggests that this is less likely to be a major impact of the drip water on fungal community structure in light of the strong impact of distance from the entrance of the cave on fungal diversity and richness. It might be expected that if drip water were the primary determinant of fungal community structure, fungal diversity and richness would be independent of such a distance gradient. The results from this study do not preclude a possible fungal propagule origin effect of drip water on fungal community structure because the collected water was not examined for the presence of fungal propagules. In past studies, Vaughan et al (2011) and Nováková (2009) successfully cultured an array of fungi from mineral surfaces that were actively dripping. However, in neither study did the authors determine if the fungal propagules were distinctly associated with the mineral surface itself or the water on it.

The strong negative correlation between increasing distance into the cave and fungal OTU richness and diversity suggests that at least a portion of the fungi surveyed in this study are entering the cave through the natural entrance. This would mean that sites closer to the cave entrance would host more diverse and rich fungal populations. This idea is further supported by the current study in that the taxonomic compositions of the fungal community are significantly different at sites closer to the natural entrance from those distal to the natural entrance. Other studies have suggested that the primary route of fungal entry into caves is through air/water/faunal flow through natural entrances of caves. A similar gradient was observed in the Cave of Nerja in Spain (Decampo et al.,

2011). In their work, Decampo et al. describe gradients of particular spore types present in high level outside of a cave that decrease at sampling sites located successively distal to the cave entrance. They also describe the presence of pollen grains in the cave, suggesting that the cave entrance is a significant source of airborne particulates. Jurado et al. (2008) suggest that there is an association between insects and the dissemination of fungal propagules in caves, and a number of surveys have been made of the fungal flora on cave dwelling insects (Gunde-Cimerman et al., 1998; Benoit et al., 2004). Shapiro and Pringle (2010) suggest that disturbance from outside the cave may be required for fungal community establishment. Due to the fact that the transect in the present study begins 275 m into the cave, a necessity to avoid areas possibly impacted by seasonal bat populations in a portion of the cave, this study cannot determine if fungal diversity and richness are highest at the entrance of the cave. There clearly is a gradient of fungal diversity leading away from the entrance of the cave, but to address the specific issue of

83 cave-fungal origins, future work will need to observe multiple transects extending from the rear of the cave to its natural entrance.

In conclusion, fungal communities from speleothems and rock walls differ in richness and diversity, but they do not differ significantly in community membership and taxonomic composition. Further investigation is needed to see if this same relationship is found among different speleothem species. These results indicate that fungi in caves like

Kartchner are equally capable of inhabiting stalactite and rock wall surfaces. Differences observed in community structure between these substrates are likely attributable to factors influencing the local distribution of fungal propagules such as micro air currents or animal activity. In addition, this study demonstrates a positive relationship between fungal diversity, richness, and increasing proximity to the natural cave entrance along the sampled transect. This suggests a possible relationship between the cave entrance and fungal origin. Lastly, there was no effect of cave drip water nutrient content on fungal community structure at a large spatial scale. This study did not decipher the role of drip water on fungal community structure. It highlights the need for studies that explore the impacts of cave drip water on microbial community structure at multiple spatial scales.

Acknowledgements

The authors would like to thank Arizona State Parks, the RIM Volunteers, and the

Kartchner Caverns Cave Unit for all of the support they have provided in sampling and teaching us about Kartchner. A particularly big thanks to Steve Willsey for leading the sampling expeditions and Bob Casavant for organizing them. We would also like to thank Jana U’Ren for her help with the statistical analyses. Many thanks as well to Mary

Kay Amistadi at ALEC for analyzing the drip water samples. This study was supported in part by the College of Agriculture and Life Sciences, University of Arizona, by the

Arizona State Parks system, and by the National Science Foundation (NSF-MCB

#0604300).

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Tables

Table 1. Environmental variables for sampling sites. Nutrients listed are from speleothem samples. Missing data is marked by a “-”.

Distance pH of Drip Water Nutrient Content [ug/L-1] Site from Natural Temperature [CO2] Drip Entrance (m) Water [TOC] [TN] [PO4] [Na] [Fe] [Mn] [K] Site 1 760 65.95 1816 7.8 36620 8233 202 130000 906 9 8670 Site 2 685 68.57 1865 8 26356 5761 0 106891 197 4 3153 Site 3 610 66.68 - 7.7 28000 6497 0 144328 71 1 2994 Site 4 460 65.97 1895 7.8 52880 10553 0 99537 1720 5 3482 Site 5 335 67.03 1911 7.8 14883 8061 0 80500 90 0 2176 Site 6 360 - - 7.7 ------Site 7 340 67.73 1902 8.1 52086 11924 0 102275 179 2 2485 Site 8 275 66.79 - 7.9 55291 3741 0 126572 154 5 3061

Supp Table 1. Sequencing summaries for individual samples, samples sites by substrate, each substrate, and for the total study.

Raw Number of OTUs number Number of Fisher's of sequences 100% 97% 95% 90% Alpha at Sample Site Substrate sequences post filter 95% OTU SP1 Site 1 Speleothem 11,300 8,005 (70%) 2,213 776 196 97 36.29 SP2 Site 1 Speleothem 6,324 3,900 (61%) 1,158 368 107 48 20.34 SP3 Site 1 Speleothem 49,517 33,965 (68%) 6,787 1,095 204 66 28.85 Site 1 Summary Speleothem 67,141 45,870 (68%) 9,917 1761 356 147 47.28 SP4 Site 2 Speleothem 70,074 46,864 (66%) 7,070 1,091 218 75 29.59 SP5 Site 2 Speleothem 21,743 17,058 (78%) 1,276 206 79 34 10.71 SP6 Site 2 Speleothem 23,231 15,868 (68%) 3,319 726 183 59 29.02 Site 2 Summary Speleothem 115,048 79,790 (69%) 11,466 1743 355 124 43.00 SP7 Site 3 Speleothem 34,162 27,733 (81%) 2,713 661 165 83 23.30 SP8 Site 3 Speleothem 11,218 8,612 (76%) 1,561 238 89 51 13.83 SP9 Site 3 Speleothem 15,724 12,003 (76%) 2,575 805 127 49 19.82 Site 3 Summary Speleothem 61,104 48,348 (79%) 6,581 1318 265 129 34.00 SP10 Site 4 Speleothem 15,525 11,652 (75%) 2,350 741 163 76 26.83 SP11 Site 4 Speleothem 30,432 5,512 (18%) 1,976 795 200 78 40.68 SP12 Site 4 Speleothem 9,541 5,932 (62%) 1,351 506 138 61 25.26 Site 4 Summary Speleothem 55,498 23,096 (42%) 5,214 1394 327 144 49.00 SP13 Site 5 Speleothem 14,959 12,422 (83%) 2,927 921 316 172 59.02 SP14 Site 5 Speleothem 15,387 12,709 (82%) 3,348 846 292 171 53.30 SP15 Site 5 Speleothem 19,757 14,935 (75%) 3,379 826 368 234 68.24 Site 5 Summary Speleothem 50,103 40,066 (80%) 9,097 1964 680 394 100.00 SP16 Site 6 Speleothem 21,563 17,517 (81%) 4,182 1,006 317 182 54.96 SP17 Site 6 Speleothem 22,930 19,207 (83%) 4,089 1,259 396 217 70.59

95 95

SP18 Site 6 Speleothem 13,774 11,392 (82%) 2,639 787 296 168 55.55 Site 6 Summary Speleothem 58,267 48,116 (83%) 10,245 2164 669 396 96.00 SP19 Site 7 Speleothem 12,715 9,637 (75%) 2,605 812 255 149 48.06 SP20 Site 7 Speleothem 15,055 11,586 (76%) 3,070 885 250 129 45.01 SP21 Site 7 Speleothem 35,064 27,386 (78%) 4,428 964 281 157 43.61 Site 7 Summary Speleothem 62,834 48,609 (77%) 9,638 2044 542 311 74.00 SP22 Site 8 Speleothem 23,360 19,232 (82%) 5,042 1,043 243 116 39.21 SP23 Site 8 Speleothem 22,310 18,544 (83%) 4,492 1,280 316 149 54.11 SP24 Site 8 Speleothem 24,937 18,591 (74%) 5,193 1,319 332 165 57.41 Site 8 Summary Speleothem 70,607 56,367 (80%) 13,947 2503 580 293 78.39 390,262 Speleothem Summary 540,602 68,918 6750 1755 147 186.40 (72%) W1 Site 1 Rock Wall 31,986 26,865 (83%) 3,627 546 99 22 12.96 W2 Site 1 Rock Wall 20,232 16,543 (81%) 2,227 274 73 45 9.83 W3 Site 1 Rock Wall 5 1 1 1 1 1 0 Site 1 Summary Rock Wall 52,223 43,409 (83%) 5,844 808 159 55 19.60 W4 Site 2 Rock Wall 0 0 0 0 0 0 - W5 Site 2 Rock Wall 6 6 4 2 2 2 1.051 W6 Site 2 Rock Wall 0 0 0 0 0 0 - Site 2 Summary Rock Wall 6 6 4 2 2 2 1.051 W7 Site 3 Rock Wall 0 0 0 0 0 0 - W8 Site 3 Rock Wall 29,167 23,535 (80%) 3,440 745 182 66 26.86 W9 Site 3 Rock Wall 8,394 6,291 (74%) 1,656 253 63 30 9.733 Site 3 Summary Rock Wall 37,561 29,826 (79%) 5,034 918 210 82 27.03 W10 Site 4 Rock Wall 12,376 10,427 (84%) 1,204 417 57 5 7.94 W11 Site 4 Rock Wall 23,253 19,722 (84%) 2,643 651 72 7 9.42 W12 Site 4 Rock Wall 42,729 36,498 (85%) 3,254 466 100 37 12.54 Site 4 Summary Rock Wall 78,358 66,647 (85%) 6,373 863 139 43 15.41

96 96

W13 Site 5 Rock Wall 19,540 15,195 (77%) 3,404 935 321 184 57.52 W14 Site 5 Rock Wall 14,276 11,563 (80%) 2,484 467 188 124 31.89 W15 Site 5 Rock Wall 20,841 16,759 (80%) 3,361 708 254 141 42.47 Site 5 Summary Rock Wall 54,657 43,517 (80%) 8,824 1641 562 318 77.46 W16 Site 6 Rock Wall 12,240 9,658 (78%) 2,577 561 212 125 38.31 W17 Site 6 Rock Wall 31,805 26,163 (82%) 4,046 922 262 128 40.48 W18 Site 6 Rock Wall 22,685 17,583 (77%) 3,448 510 168 93 25.73 Site 6 Summary Rock Wall 66,730 53,404 (80%) 9,731 1588 464 242 63.37 W19 Site 7 Rock Wall 27,458 22,428 (81%) 3,741 923 267 137 42.60 W20 Site 7 Rock Wall 18,575 13,833 (74%) 3,017 830 267 134 46.93 W21 Site 7 Rock Wall 17,210 9,284 (53%) 2,080 642 212 117 38.65 Site 7 Summary Rock Wall 63,243 45,545 (72%) 8,294 1772 494 266 68.38 W22 Site 8 Rock Wall 24,995 20,435 (81%) 4,798 918 257 126 41.43 W23 Site 8 Rock Wall 28,029 23,607 (84%) 3,244 659 204 116 30.69 W24 Site 8 Rock Wall 32,747 26,686 (81%) 4,715 1,007 250 109 38.16 Site 8 Summary Rock Wall 85,771 70,728 (82%) 12,161 1840 478 243 57.49 353,082 Rock Wall Summary 438,549 52,018 5084 1327 55 140.70 (80%) 743,344 Totals across all samples 1,022,447 114,986 8,506 2,291 1,182 292.10 (72%)

Supp. Table 2. Pyrosequencing fusion primer sequences used in this study and their associates samples.

Figure Legends.

Fig. 1 A map of Kartchner Caverns. The transect is demarcated by the dashed line, and it runs for ca. 475 m in a low-impact section of the cave that is not influenced by the seasonal bat population. At each sampling site, there are three speleothems (∆) and three wall samples (o).

Fig. 2 To examine sampling completeness, OTU accumulation curves were constructed for a) all samples combined, b) speleothems, and c) rock walls after 50 randomizations using an OTU definition of 95% sequence similarity. Sampling was not saturated for either speleothems or rock walls. Rarefaction of speleothems and rock walls d) shows that speleothems are more OTU rich than rock walls.

Fig. 3 Non-metric multidimensional scaling plots the relative similarities of fungal communities from each sample within a non-ordinal space. When grouping the samples based on a) substrate type (speleothem and rock wall) the samples overlap. However, when comparing samples from the front of the transect to the back of the transect b) two distinct clusters are formed.

Fig. 4 Least squares regression of fungal OTU a) richness and b) diversity by distance along the sampling transect. As distance from the natural entrance increases, both OTU richness and diversity decrease. It was also found that c) as distance between samples increased, community similarity decreased.

100

Fig. 5 The relative percentage of OTU sequences belonging to fungal subphylum. The number of OTUs for each subphylum is listed next to its name in the figure key. There are no significant differences between a) speleothems and rock wall samples. However, when comparing the distributions of the front sampling sites to those of the back sampling sites, there are differences in community taxonomic distribution (indicated by an *).

Fig. 6 A principal component analysis (PCA) was conducted to examine the relationships among observed nutrient concentrations and sample sites. The PCA biplot reveals the relationship among measured drip water nutrient content and sites for water collection.

The position of sampling sites in relation to the arrows for each nutrient depicts the relative concentration of each nutrient in each sample. Examination of the biplot reveals that separation of sites was strongly correlated with iron and sodium concentrations. The biplot also reveals that separation among samples reflects sampling sites rather than substrate types.

Fig. 7 CCA triplots testing whether observed environmental variables have a significant effect on observed community composition. The triplot depicts the effect of sampling site on community structure. Sampling site was the only variable that produced significant axes indicating that it has a significant effect on community composition. The greatest variation was seen among samples from site 2, followed by samples from sites 5,

101

7, and 3. Separation of sampling sites, other than site 2, was correlated with the second canonical axis.

102

Figure 1

103

Figure 2 a, b

104

Figure 2 c, d

105

Figure 3a

106

Figure 3b

107

Figure 4a

108

Figure 4b

109

Figure 4c

110

Figure 5

111

Figure 6

112

Figure 7

113

APPENDIX C

ASSESSING FUNGAL COMMUNITY STRUCTURE WITHIN BAT GUANO FROM KARTCHNER CAVERNS USING MULTIPLEXED 454 PYROSEQUENCING

In preparation for submission to Microbial Ecology 114

Title: Assessing fungal community structure within bat guano from Kartchner Caverns using multiplexed 454 pyrosequencing

Michael Joe Vaughan, Will Nelson, Cari Soderlund, Raina M. Maier, and Barry M. Pryor

Abstract

Kartchner Caverns, a carbonate cave located near Benson, Arizona, USA, is a popular tour cave that houses a seasonal population of cave myotis bats, Myotis velifer. Ongoing conservation efforts in the cavern have been focused on describing biotic communities at all levels, including microbial communities associated with the resident bats. Bat guano fungal community diversity and composition was examined four times over one year based upon fungal operation taxonomic units (OTUs) derived from 454 FLX Titanium sequencing of internal transcribed spacer 1 (ITS1) sequences. Changes in fungal OTU taxonomic identity across time and changes in bat guano nutrient concentration and its effect on fungal community composition also was examined. Sequencing revealed 1429

OTUs based on 95% sequence similarity. There were no significant differences in OTU richness, OTU diversity, or fungal OTU taxonomic membership among sampling times.

Similarly, there was no observed significant effect of time since last bat presence on fungal richness or diversity. Sampling times did not differ significantly in the concentration of measured nutrients. However, two sites were significantly different from the other three in terms of nutrient concentration and community composition.

These differences were related to reduced guano accumulation through the course of the

115 sampling season. Thus, fungal community membership was significantly correlated with guano nutrient content.

116

Introduction

Research elucidating the critical roles that microbes play in subterranean environments has led to increasing interest among speleologists regarding the diversity and functioning of microbial communities in caves (Barton and Jurado, 2007). The structure and distribution of mycological communities is emerging as an area of key interest to cave conservationists and wildlife biologists. Researchers studying the endemic arthropod communities have recognized that fungi constitute an essential food source in many subterranean food webs (Harris, 1973; Dickson and Kirk, 1976). Cave conservationists have also reported the role of fungi in the destruction of important anthropological artifacts in caves (Jurado et al., 2008; Jurado et al., 2010; Martin-Sanchez et al., 2012; Bastian and Alabouvette, 2009). Other researchers have highlighted that fungi may be critical in affecting and modifying mineral deposits in some caves (Went,

1969). Finally, the recent emergence of white nose syndrome (WNS), a fungal disease of bats, in North America has further highlighted the need to better understand the diversity and distribution of fungi in cave systems (Blehert et al., 2009; Gargas et al., 2009;

Lindler et al., 2010; Lorch et al., 2011).

Caves are oligotrophic environments. As such, they provide unique opportunities for the study of biotic communities in carbon-limiting environments (Culver and Pipan,

2009). Heterotrophic cave organisms, like fungi, are reliant on carbon input from outside of the cave system. In caves that support bat populations, bat guano is a major source of externally derived carbon and is a keystone resource for many troglodytic species

(Culver, 1982; Culver and Pipan, 2009). Guano piles represent rich islands in an

117 otherwise nutrient-poor environment, supporting both generalized troglodytic and guano specialized, guanobiont, communities (Deharveng and Bedos, 2000, Culver and Pipan,

2009). The mycoflora of guano is responsible for the mobilization of guano nutrients to higher trophic levels and forming the base of many cave-based food webs (Fletcher 1975,

Dickson and Kirk, 1976; Fierriera et al., 2007). Although a number of studies have focused on exploring the unique morphologies and interactions of invertebrate guano pile community members, little attention has been paid to the composition of fungal communities. Delineating the structure and functioning of guanophilic fungi would add to our understanding of the complex interactions taking place in caves supporting bat populations (Moulds, 2006).

Studies examining fungi associated with bat guano typically address fungi that impact human health. Bats and bat guano are reservoirs for zoonotic disease agents, such as Histoplasma capsulatum, and studies on the distribution of these fungi are numerous (Sacks et al., 1986; Taylor et al., 1999, Cano and Hajjeh, 2001). Others have examined the fungal spore loads of cave air (Docampo et al., 2011). Several studies have attempted to quanitify the culturable fungal diversity of bat guano, resulting in new fungal records (Nieves-Rivera et al., 2009; Nováková , 2009; Nováková and Kolařík,

2009, Tsuneda et al., 2011). However, more information regarding fungal community structure on guano piles is lacking.

The literature concerning fungal communities from bat guano is sparse, but fungal succession and community composition have been studied in detail on herbivore and carnivore dung from surface environments (Harper and Webster, 1964; Webster, 1970;

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Wicklow, 1992; Richardson, 2001). On these substrates, general patterns of fungal sporocarp succession have been established, which tend to reflect the time needed to produce fruiting bodies (Richardson, 2002). Whether or not this succession is indicative of mycelial succession on dung has not been addressed (Fryar, 2002). In some fungal/substrate systems, such as decaying wood, researchers directly observed mycelial succession due to unique characteristics of the system (Rayner and Todd, 1979). Direct observation may not be feasible for all systems, and other approaches, such as community profiling, could reveal changes independent from sporulating structure succession (Fryar, 2002). These succession studies have shown that coprophilous fungal communities are influenced by physical parameters of the dung, including nutrient content, the diet of the depositor and the environment in which the substrate was deposited (Wicklow and Moore, 1974; Nyberg and Persson, 2002; Kruys and Ericson,

2008).

The structure of fungal communities has been examined in environments ranging from mineral surfaces to the interior of living plant leaves (Gleeson et al., 2005; U’Ren et al., 2012). Initially, many of these studies used traditional culture based methods

(Blackwell, 2011). However, these approaches cannot capture fastidious or culture- recalcitrant fungal species. With the advent of high volume sequencing technologies, such as 454 pyrosequencing, researchers have taken advantage of a new set of bioinformatic tools to estimate fungal community structure using thousands of sequences generated from the DNA of entire communities (Metzker, 2009). Using these

119 approaches, mycologists are able to measure more completely fungal diversity and community structure, including culturable and non-culturable taxa (Blackwell, 2011).

Kartchner Caverns is a wet, biologically vibrant carbonate cave located in an escarpment of Mississippian Escabrosa limestone in the Whetstone Mountains near

Benson, AZ, USA (N31° 50’ 08”, W110° 20’ 37”). It is maintained and operated as part of Kartchner Caverns State Park. The cave is composed of more than 3 km of passage way and is split into two cave complexes, the Big Room and the Rotunda/Throne Room

(Hill, 1999). The Big Room complex, which houses the current natural entrance, hosts a seasonal population of cave myotis, Myotis velifer (Buecher and Sidner, 1999). The presence of the M. velifer colony is seasonal, arriving in April to give birth and departing in September (Buecher and Sidner, 1999). The bat colony is composed of ca. 1,000 -

2000 individuals. The bats occupy one section of the cave, the Big Room Complex, during their seasonal visitation. While the bats are present, cave tours for the Big Room complex are unavailable, and visitation is highly restricted.

Before developing the cave, biological and environmental surveys of Kartchner

Caverns were conducted to provide baseline information for the responsible management of the cave. These studies have continued after opening the cave to the public. Among these, microbiological investigations were conducted using both culture and non-culture based methods (Legatzki et al, 2011; Vaughan et al., 2011; Legatzki et al, 2012). These studies have focused largely on mineral surface communities in the cave. Given the importance of bat guano in many cave ecosystems, a microbiological characterization of

120 guano piles would provide critical information for the conservation of the Kartchner cave system.

This study examines fungal communities from bat guano and the changes that occur in fungal community structure through one year between periods when the bats are present and absent. Investigations were conducted using 454 pyrosequencing of community-generated internal transcribed spacer 1 (ITS1) sequences, a marker useful in assessing fungal community structure. It was hypothesized that fungal richness and diversity would decrease as the guano piles decomposed in the absence of bats.

Taxonomic composition among sampling times was expected to differ as well, reflecting successional changes observed on herbivore dung. In addition, guano pile nutrient content at each sampling time was measured to assess its effects on fungal community composition. It was expected that changes in nutrient concentrations found in bat guano among sampling times would be correlated with shifts in fungal community membership and structure.

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Materials and Methods

Sampling Sites

All sampling was conducted in a single section of the cave known as the Big Room

Complex. After consultation with park staff, samples were taken from the five distinct

M. velifer guano piles considered to be seasonally active (Fig 1). While two other species of bat are known to inhabit Kartchner Caverns, they are present as a few individuals occupying the section of the cave near the natural entrance (Buecher and Sidner, 1999).

Each pile was named for its proximity to prominent features in the cave (Table 1).

Before sampling, pile dimensions (base width and length, and depth) were measured using a tape measure and a bronze rod to probe depth (Table 1). Guano pile depth was measured again during the final sampling to estimate pile activity. Information regarding ambient air conditions in the Big Room Complex was extracted from environmental monitoring data curated by Kartchner Caverns Cave Unit staff (Toomey and Nolan,

2005; Blasch, 2011).

Fungal community sampling

Samples were collected on four dates starting April 20th, 2010, and ending in April of

2011. The first sampling was conducted before the arrival of the bats for their 2010 brooding. The next sampling on September 1st, 2010, was conducted immediately after the bats had departed Kartchner for their annual migration to their hibernacula. Sampling was conducted again on December 8th, 2010 and on April 11th, 2011, before the bats returned to Kartchner. At each guano pile for each sampling date, triplicate guano samples were taken using a sterilized spoon. Samples were taken from different positions

122 across the guano piles and included fecal matter from the surface to the bottom of the pile. In cases where the pile was greater than 10 cm deep at the coring location, only fecal material from the top 10 cm of the pile was recovered. Spoon core samples were placed in sterile plastic sample bags. Samples were transported on ice from the sampling site to the laboratory and then placed at 4C until processed for DNA extraction.

DNA extraction, amplicon preparation, and sequencing

Before DNA extraction, each guano core was homogenized in its sample bag.

Community DNA was extracted from 0.5 g of guano from each of the three core samples from each guano pile. Remaining guano was placed at -80C for storage until processing for physical and nutrient measurments. DNA was extracted using a FastDNA

Spinkit for Soil (MP Biomedicals, LLC, Solon, OH) following standard manufacturer protocol with the modification that silica binding matrix was left to dry overnight in the spin filters after washing with the proprietary SEWS solution before DNA elution. In addition, the silica binding matrix was washed with the SEWS solution twice to help remove pigments and polymerase chain reaction (PCR) inhibitors from the DNA extraction.

DNA quality was inspected visually by transillumination in an EpiChem3 imaging system (UVP, San Gabriel, CA) following gel electrophoresis and staining with ethidium bromide. DNA extractions were then subjected to a dilution of 1:20 v/v using sterile water to minimize the effect of PCR inhibitors in downstream applications.

Extractions were then quantified by DNA fluorescence using Quant-it PicoGreen dsDNA stain (Molecular Probes Inc, Eugen, OR) and a Synergy H1Hybrid multi-mode

123 microplate Reader (BioTek, Winooski, VT) reading the fluorescence wavelengths 480 nm (excitation) and 520 nm (emission) as per manufacturer instructions. DNA solutions were then brought to a standard concentration of 1 ng/ul for PCR reactions.

Three independent PCR reactions were performed for each guano DNA extract.

For this study, twenty fusion primers were designed with pile/sampling date specific nucleotide bar codes (Supp. Table 2). Guano core extracts from the same pile/date were given the same multiplex barcode. PCR reactions targeted the internally transcribed spacer 1 (ITS1) variable region of the rDNA tandem repeat using the fungal specific primer sequence ITS1F, (CTT GGT CAT TTA GAG GAA GTA A), in the forward fusion primers and the reverse primer ITS2, (GCT GCG TTC TTC ATC GAT GC)

(White et al., 1990). The ITS region of fungal rDNA was selected for this study because of its utility as a genetic locus for fungal identification and environmental sequencing

(Nilsson et al., 2009; Schoch et al., 2012). The forward fusion primers contained the 25 bp pyrosequencing primer A (PPA), a 10 bp sample specific sequence tag (ST), and the primer ITS1F (5’- (PPA) CGT ATC GCC TCC CTC GCG CCA TCG A– (ST) NNN

NNN NNN N– (ITS1F) CTT GGT CAT TTA GAG GAA GTA A -3’). Each 25 uL PCR reaction contained 0.08 uM of each primer, 0.2 mM of each dNTP, 1X buffer (containing

10 mM Tris-HCl (pH 8.8), 50 mM KCl, and 0.08% Nonidet P40), 2.5 mM MgCl, 2.5 ug bovine serum albumin (BSA), 1 U of DreamTaq DNA polymerase (Fermentas Inc.,

Burlington, Ontario), and 0.08 ng uL-1 environmental DNA. In cases where PCR reactions failed, DNA extracts were subjected to additional serial dilutions to attenuate the effect of PCR inhibitors. After confirming PCR success using transillumination as

124 previously described, individual reactions were subjected to PCR clean up using the

QIAquick PCR purification kit (Qiagen Inc., Valencia, CA) as per manufacturer instructions. Each PCR reaction was then quantified by DNA fluorescence using Quant- it PicoGreen dsDNA stain as described above. After quantification, the three individual

PCR reactions for each guano core were combined in equimolar DNA concentrations.

The three pooled guano core samples were then similarly combined per guano pile/sampling time. Samples were subjected to multiple PCR reactions and pooled in an effort to alleviate possible PCR bias. Each of the 20 pooled guano pile/time samples were then combined into one pool of DNA at an equimolar DNA concentration. This pool of DNA was then brought to a DNA concentration of 1x109 DNA molecules per ul for submission to the University of Arizona Genetics Core for sequencing on the

Titanium GS FLX 454 platform. Multiplex sequencing was conducted on both sides of one plate with the same pooled amplicon sample.

Sequence processing and quality control

Initial sequence processing was conducted using mothur v. 1.21.0 (Schloss et al.,

2009). Sequences with ambiguous bases, quality scores lower than 30, lengths less than

150 bp or greater than 400 bp, and sequences missing both the primer and barcode sequences were removed from the data set. The data set was then dereplicated by removing sequences of 100 % sequence similarity. Sequences were then subjected to a pseudo-single linkage clustering algorithm using the mothur pre.cluster command using default parameters to help remove sequences generated due to sequencing errors and reduce the size of the data set (Huse et al., 2010). The pre-cluster sequences were then

125 aligned in the software ESPRIT using an average linkage Needleman-Wunsch pairwise alignment algorithm (Sun et al., 2009). Sequences were then clustered into operational taxonomic units (OTU) in mothur at 100, 97, 95 and 90 percent sequence similarity using the distance matrix produced by ESPRIT. The most abundant sequence per OTU was chosen as the representative sequence for each OTU. These representatives were then scanned for the presence of chimeric sequences using the Alaskan Fungal Metagenomics

Project’s pipeline chimera check tool under default settings

(http://www.borealfungi.uaf.edu/). Flagged sequences were manually checked by quarrying separate portions of those sequences against the Alaskan Fungal Metagenomics

Project’s annotated ITS database using the basic local alignment search tool (BLAST).

Sequences that resulted in parent sequences from different genera were eliminated from the data set. OTU representatives were then processed using the ITS1/ITS2 sequence extractor for fungal ITS sequences developed by Nilsson et al. (2011). Sequences that did not correspond to ITS1 were removed from the data set.

Fungal community assessments

For all analyses, a fungal OTU definition of 95% similarity was used unless otherwise reported. To examine sampling efficiency and completeness, species accumulation curves and estimated true species richness were calculated using the software package EstimateS v. 8.2 (Colwell, 2005). Calculations were made using 50 randomizations of the observed data. Sampling was considered complete if the bootstrap species richness estimator entered the 95% confidence intervals for the observed species accumulation. If the estimated true species richness fell outside of the 95% confidence

126 interval for the observed species richness, subsequent analyses were conducted with a dataset excluding rare OTUs (n = 1) unless noted (Sogin et al., 2006).

Observed species richness and Fisher’s alpha () were computed for each guano pile/time sample using the software package PAST v 2.01 (Hammer et al., 2001).

Differences in species richness and diversity among the different sampling times were examined in PAST using repeated measures analysis of variance (ANOVA). The dataset including rare taxa was used in the computation of alpha diversity statistics.

A cluster analysis based on the Jaccard similarity index (defined as the shared number of OTUs between two samples divided by the total number of OTUs in both samples) was conducted to examine the similarity of fungal communities from the guano piles. Community profiles were then subjected to ordination using non-metric multidimensional scaling (NMDS). An analysis of similarity (ANOSIM) was conducted to test a null hypothesis of no difference among the observed fungal OTU communities from guano piles across all sampling times and for communities from sampling dates across all piles using 10,000 permutations. Both a presence/absence based metric

(Jaccard similarity index) and an abundance-based metric (Morisita-Horn index) were employed as measures of similarity for these analyses to address concerns about discrepancies between read abundance and actual OTU abundance (Amend et al., 2010).

A least squares regression was conducted to examine the effect of increasing time post bat presence during the 2010-2011-tour season on fungal OTU diversity and species richness using PAST. When examining the effect of distance between sampling sites on

127 community similarity, diversity indices (Jaccard and Morisita -Horn) were square root transformed and distances were square root + 1 transformed to attain normality.

Fungal OTU sequences were subjected to BLAST searches against the Alaskan

Fungal Metagenomics Project’s annotated ITS database to obtain a subphylum and class level identification for each OTU. The top five BLAST hits were examined for each

OTU. All BLAST hits with bit scores less than 100 were excluded from consideration and given the status of “poor BLAST match”. The alignments for sequences with bit scores between 101 and 200 were examined by eye and given the status of either “poor

BLAST match” or their associated taxonomic description. In cases where none of the five BLAST hits agreed at the class level, each BLAST hit was examined closely to confirm that a current taxonomy was being used. In these cases, a consensus designation was made among all hits. In cases where the top five BLAST hits matched unidentified fungal voucher sequences, the isolates were given the status of unidentified. The null hypothesis that the distribution of OTUs among represented fungal classes was the same among the different sampling dates was tested using a chi-square test on percent normalized data for two sampled datasets using the software PAST (U’Ren et al., 2010).

Using the same method, a similar hypothesis was tested that compared OTU representation of fungal classes among the individual guano piles. For both tests, alpha levels were adjusted using a Bonferroni correction for pairwise comparisons, and the degrees of freedom were reduced by one to account of the use of percent normalized data.

Analysis of guano nutrient concentrations and effects on fungal community structure

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At the conclusion of sampling, guano that was not used for DNA extraction was removed from storage at -80C. The individual core samples from each pile were combined, and 1g (wet weight) was used to measure guano pH using previously published methods (Luebbe et al., 2011). The remaining guano was weighed and placed in a drying oven. Dried guano samples were then weighed and pulverized using a mortar and pestle. Dried samples were then packaged in paper envelopes and submitted to the

Water Quality Center Laboratory at the University of Arizona’s Environmental Research

Laboratory for analysis of nutrient content. Each sample was analyzed for total carbon, nitrogen, phosphorus, sodium, potassium, calcium and magnesium.

A repeated measures ANOVA was used to assess whether levels of the measured nutrients changed over time. A principal component analysis (PCA) was then conducted in PAST that examined the correlations among the guano pile samples at each sampling time and the observed nutrient concentrations. The PCA allowed for the identification of environmental measurements that had a high level of explanatory power for discriminating among samples. The relationship of measured nutrient concentration to community composition was then analyzed using canonical correspondence analysis

(CCA) in PAST. CCA is a direct gradient analysis used to test hypotheses concerning environmental factors and observed community structure (Legendre and Legendre, 1998;

Palmer, 1993; ter Braak, 1986). Significance of CCA axes were computed using a permutation analysis (permutations= 1,000).

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Results

Sequence processing and quality control

A total of 1,019,188 sequences were generated during our sequencing effort

(Supp. Table 1). There was no significant difference in sequence recovery from samples among sampling times or from individual guano piles (ANOVA, F(3,15) = 1.837, p =

0.1776 and F(4,14) = 0.9572, p = 0.4643 respectively). Initial filtering and quality control created a data set of 769,973 sequences. These sequences were collapsed into 42,755 genotypes (100% sequence similarity) and 1,427 OTUs based on 95% sequence similarity (Supp. Table 1). Sequences from the April 20, 2010 sampling (n = 251,606) represented 16,382 genotypes and 1,027 OTUs (23.8% were singletons). Sequences from samples taken in September (n = 170,371) represented 11,942 genotypes and 798 OTUs

(23.8% were singletons). The 162,224 sequences recovered from samples in December of 2010 represented 11,829 genotypes and 791 OTUs (20.7% were singletons).

Sequences recovered from the final sampling date (n = 162,900) represented 11,598 genotypes and 772 OTUs (20.9% were singletons).

Fungal community assessments

Alpha diversity

The sampling effort was insufficient to capture the full ITS1 based OTU richness across all samples (Fig. 2a). A dataset excluding rare taxa was used when comparing diversity among sample times and guano piles. This data set was used to account for the inability to confirm the rare status of some OTUs due to incomplete sampling. Samples taken during December 2010 generated the fewest number of sequences (n=162,224).

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Direct comparison of all sampling times by rarefaction revealed that there were no significant differences in OTU recovery (December 2010, 791 OTUs, 95% confidence interval (CI) = 762.6 – 819.4; April 2010, 806 OTUs, 95% CI = 775.0 – 837.0) (Fig. 2b).

Differences in fungal communities

A repeated measures analysis of variance (rmANOVA) revealed that there were no differences in fungal OTU richness or diversity among the sample times (rmANOVA,

F(4,10) = 1.799, p = 0.2175, and F(4,10) = 1.176, p = 0.3901 respectively). To meet the requirements for the rmANOVA, samples from one pile, Maternity Roost, were excluded because of a missing sample at one of the sample times. The amount of time that had elapsed since the last M. velifer occupation of the cave had no effect on either observed

OTU richness or diversity (Least squares regression, R2 = 0.0849, P = 0.3119, and R2 =

0.0134, P = 0.6931 respectively).

The similarity of fungal communities among sampling times was visualized using non-metric multidimensional scaling (NMDS). This analysis revealed (stress = 0.1466) that the space occupied by each time period overlapped every other time period (Fig. 3a).

An analysis of similarity (ANOSIM) confirmed that there were no significant differences in community composition across the four sampling times (ANOSIM, Jaccard distances: mean within rank = 95.39, mean between rank = 83.5, R = 0.1391, P = 0.981, Morisita-

Horn distances: mean within rank = 95, mean between rank = 83.6, R = 0.1333, P =

0.9649). Cluster analyses based on the same distance measures revealed no distinct clustering of samples based on sampling time (data not shown).

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The NMDS analysis did reveal distinct groupings when the samples are examined by the identity of the pile sampled (Fig. 3b). An analysis of similarity confirmed that the guano piles at Kartchner towers and Sharon’s Saddle were significantly different from those at Sombrero, Maternity Roost, and Lunch Spot (ANOSIM, Jaccard distances: mean within rank = 42.97, mean between rank = 94.07, R = 0.5977, P < 0.0001, Morisita-Horn distances: mean within rank = 40.44, mean between rank = 94.54, R = 0.6327, P <

0.0001). The same groupings were observed using cluster analysis (data not shown)

There was no significant difference in the distribution of OTUs among fungal classes at any given sampling time (Pairwise Chi2 comparisons, Bonferroni corrected alpha = 0.008, Table 2) (Fig. 4a). However, the taxonomic composition of individual guano piles was significantly different (Pairwise Chi2 comparisons, Bonferroni corrected alpha = 0.005, Table 2) (Fig. 4b). Piles that had significantly different taxonomic compositions mirrored the clusters found in the NMDS plot when grouping by pile.

Analysis of environmental conditions and effects on fungal community structure

The nutrient content of guano samples (Supp Table 3) did not differ significantly with time (Table 3). When grouping guano samples by source pile, significant differences in the amount of nitrogen, sodium and potassium were observed (rmANOVA,

F(4,10) = 7.259, p = 0.0052, F(4,10) = 13.7, p = 0.0011, and F(4,10) = 6.571, p = 0.0120 respectively) (Table 3). Principal component analyses (PCA) conducted to further examine the relationships among measured guano nutrients and sample sources (time and site) constructed principal axes accounting for 99.14 % of observed variation in the data set (First axis eigenvalue = 7.3918, 96.75 % of variation, Second axis eigenvalue =

132

0.1831, 2.39% of variation). There was no separation of samples along the first axis based on sampling time (Fig. 5a), but separation was observed between the Kartchner towers and Sharon’s Saddle piles and the remaining three piles (Fig. 5b). PCA revealed that total nitrogen and sodium were highly correlated with the differences between these two groups (Fig. 5a,b).

Canonical correspondence analysis (CCA) revealed distinct differences in community structure among the Kartchner towers pile, the Sharon’s Saddle pile, and the remaining three piles in relation to nutrient concentrations (CCA permutation = 1000, trace = 1.567, p = 0.0099) (Fig. 6). No separation of samples by sampling time was observed. The CCA triplot revealed that separation along the first axis (eigenvalue =

0.6394, p = 0.0099) was positively correlated with total calcium and negatively correlated with total nitrogen, sodium, and potassium. Separation along the second axis (eigenvalue

= 0.3832, p = 0.0297) was correlated with total phosphorus, magnesium, and carbon (Fig.

6).

Two of the bat roosts and their associated guano piles, Sharon’s Saddle and

Kartchner towers, experienced less activity than the other three. This was indicated by the amount of guano that accumulated over the course of the 2010 bat occupation (Table

1) and reports from park rangers. Both of these guano piles showed distinct differences from the other sampling sites in community structure, taxonomic composition, and nutrient content. As a result, analyses of OTU diversity and distribution were conducted excluding these two guano piles in addition to analyses conducted including them. There was no significant difference in richness or diversity among sampling times (rmANOVA,

133

F(2,6) = 0.6314, p = 0.5777, and F(2,6) = 0.7701, p = 0.5213 respectively) when examining only the more active piles. No effect of days after bat presence on guano OTU richness or diversity was observed for the more active piles (LSR, R2 = 0.0833, p = 0.4879, and R2

= 0.0057, p = 0.8583). More active guano piles did not differ significantly in any of the measured nutrient concentrations (Table 3). Canonical correspondence analysis was unable to resolve significant axes for the data set excluding less active piles (CCA permutation = 1000, trace = 1.03, p = 0.3663).

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Discussion

This study assessed changes in the fungal community from bat guano though the course of a year in Karchner Caverns, Arizona. Three predictions were tested: (1) fungal richness and diversity will decrease with increasing time after bat presence; (2) fungal community composition will shift over time; and (3) changes in fungal community diversity and composition will depend on bat guano nutrient content.

Guano-associated arthropod communities experience declines in diversity in relation to the seasonal absence of bat populations. Moulds (2006) demonstrated that arthropod communities from two Australian caves exhibited greater species richness and diversity in relation to higher guano moisture content and pH, which increase with the deposition of fresh guano during summer occupation in that cave. The absence of strongly guanophilic species during times of reduced or absent guano deposition have been noted for individual species of invertebrates as well (Harris, 1973). Harris observed that the mycophagus guano mite, Uroobovella coprophila, experienced rapid population declines when the deposition of fresh guano ceased and its food source became scarce, indicating fungi are similarly less abundant (Harris 1973). Given the relationship between arthropod diversity and guano deposition time, especially in light of the strong correlation between insect richness and fungal communities from bat guano (Dickson and

Kirk, 1976), it is surprising that no changes in fungal richness or diversity were observed in the present study. It was thought that the heterogeneity in roost activity among the five sampled guano piles might account for these differences, but, even excluding the less active piles, OTU richness and diversity did not differ among sampling times. While the

135 studies of bat guano invertebrate communities are not directly comparable to our study, the close association between fungal communities and invertebrate food webs on guano allows us to use these observations to provide a broader context for our results.

The distribution of taxonomic affiliations in the fungal community did not differ significantly among sampling times. Guano arthropod communities experience compositional changes though the course of guano deposition cycles. These changes are well documented between summer (guano deposition) and winter (no guano deposition) sampling seasons in Bat Cave (Moulds, 2006). The fungal succession on animal dung also indicated that shifts in fungal OTU identities would be observed through the course of the sampling season. This literature presents a succession of basal fungal lineages, followed by anamorphic then teleomorphic phases of Ascomycotina, and ending with the sporocarps of various Basidiomycotina (Richardson, 2002). The current study relied on culture independent methods to assess fungal community composition that did not involve visual inspection of fungal tissues. Previous studies of fungal succession relied heavily on direct observation of sporulating structures or mycelium (Fryar, 2002;

Richardson, 2002). The observations of sporulating structure succession might differ drastically from true mycelial succession (Rayner and Todd, 1979; Fryar, 2002;

Richardson, 2002). The culture independent methods employed in this study would have included actively fruiting/growing fungi and fungi in various states of stasis (as spores or other storage structures). This might account for the lack of observed changes in community composition, species richness, or diversity over the studied period.

136

As bat guano ages, it loses moisture, decreases in pH, and its nutritive value changes (Poulson, 2005). In work addressing both bat guano invertebrate communities and fungal dung succession, changes in community composition have been attributed to these physical parameters of the fecal substrate and its deposition environment (Kruys and Ericson, 2008). Studies of Brazilian and Australian cave guano have shown that lower species richness and diversity of invertebrate communities is associated with the lower pH and moisture content of older guano piles (Ferreira et al., 2007; Ferreira et al,

1999; Moulds, 2006). The same parameters, especially water content, have been cited as affecting fungal fruit body succession as well (Kuthubutheen and Webster, 1986).

However, since moisture and pH are strongly correlated with guano deposition, attributing community differences to any of these attributes will require manipulative experimentation. In this study, no significant differences were observed among the sampled times in regards to any of the measured guano nutrients.

A basic assumption of this project was that the guano piles in Kartchner caverns would be suitable replicates of each other. Previous work suggested that guano piles generated by the same bat species should be relatively homogeneous in terms of nutritive value (Fletcher, 1975; Poulson, 2005; Emmerson and Roark, 2007). However, the drastic differences observed between individual piles indicated that this was not true for this sampling. These differences were likely due to differences in guano accumulation and pile size, two other very important determinants of guano pile microclimates (Moulds,

2006). The variation in our sampling units, highlighted in the analysis of the nutritive

137 values of the guano piles, might have obfuscated any real differences among sampling times.

These differences, however, did highlight that the fungal communities from less active guano piles were different from those of more active guano piles. Based on literature examining changes in bat guano composition over time and fungal succession on dung communities, this would be expected. Less active piles had different nutrient content, which was highlighted by the PCA for total nitrogen. This finding is consistent with previous work (Culver and Pipan, 2009). In addition, CCA revealed that the community composition of more active piles was strongly correlated with higher total nitrogen, sodium, and potassium, and lower total calcium. Other studies have also observed that these nutrients affect fungal community structure on mineral surfaces and soil communities (Gleeson et al., 2005; Kennedy et al., 2005; Donnison et al., 2000). The current study was not designed to examine differences in fungal richness or diversity among guano piles, and the effect of these nutrients on fungal richness and diversity cannot be determined from this study. The effect of lower deposition rates for less active piles and available nutrient content could not be separated in this study. Future studies focusing on within and across-pile nutrient differences need to be conducted to further examine this relationship and its connection to fungal community structure. In addition, future studies should examine guano pH and water content closely to examine their effects on fungal community structure and for their usefulness as proxies for estimating guano pile age.

138

Acknowledgements

The authors would like to thank Arizona State Parks, the RIM Volunteers, and the

Kartchner Caverns Cave Unit for all of the support they have provided in sampling and teaching us about Kartchner. A particularly big thanks to Steve Willsey and KC Curtis for leading sampling expeditions and Bob Casavant for organizing them. We would also like to thank Jana U’Ren for her help with the statistical analyses. Many thanks to Atasi

Ray-Maitra at the University of Arizona WQC Lab for her help with the nutrient analyses. This study was supported in part by the College of Agriculture and Life

Sciences, University of Arizona, by the Arizona State Parks system, and by the National

Science Foundation (NSF-MCB #0604300).

139

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Tables

Table 1 Physical characteristic of bat guano piles examined in this study. Missing values are indicated by a (-). The range of ambient cave conditions measured during this study for the portion of the caverns where guano piles are located are presented as well.

Orthogonal Depth Accumulation Ambient cave conditions (ranges) Pile Name (abbreviation) base diameters start/finish during study pH Temperature Relative CO (ppm) (cm) (cm) (cm) 2 (C°) Humidity Sharon's saddle (SS) 205.7 x 309.9 21.9 / 22.8 0.9 3.7 Kartchner towers (KT) - 19.7 / 20.3 0.6 3.6 Maternity roost (MR) 124.5 x 119.4 54.6 / 56.1 1.5 5.1 763 - 3,617 21.61 - 22.44 95.5 - 99.5 Sombrero (Som) 190 x 172.7 30.5 / 31.6 1.1 4.2 Lunch spot (LS) 157.5 x 172.7 64.5 / 66.7 2.2 4.4

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Table 2 Pairwise Chi squared comparisons were conducted among sampling times and among guano piles to examine the distribution of fungal OTUs in represented fungal classes. Significance was assessed using Bonferroni corrected alpha values to account for multiple comparisons. Significant differences are indicated by a (*).

Comparisons between sampling times Bonferroni corrected alpha = 0.0083 for 6 comparisons Comparison df Chi2 P 4/20/2010 vs. 9/1/2010 7 8.31 0.3057 4/20/2010 vs. 12/8/2010 7 5.29 0.6252 4/20/2010 vs. 4/1/2011 7 8.08 0.3253 9/1/2010 vs. 12/8/2010 7 3.62 0.8226 9/1/2010 vs. 4/1/2011 7 1.82 0.9689 12/8/2010 vs. 4/1/2011 7 1.66 0.9761 Comparisons between guano piles Bonferroni corrected alpha = 0.005 for 10 comparisons Comparison df Chi2 P Lunch spot vs. Maternity roost 7 13.86 0.1276 Lunch spot vs. Sombrero 7 13.30 0.1495 Lunch spot vs. Kartchner towers 7 39.96 <0.0001* Lunch spot vs. Sharon’s Saddle 7 17.81 0.0375 Maternity roost vs. Sombrero 7 38.72 <0.0001* Maternity roost vs. Kartchner towers 7 34.01 <0.0001* Maternity roost vs. Sharon’s Saddle 7 39.21 <0.0001* Sombrero vs. Kartchner towers 7 51.61 <0.0001* Sombrero vs. Sharon’s Saddle 7 12.31 0.1965 Sharon’s Saddle vs. Kartchner towers 7 39.89 <0.0001*

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Table 3 Repeated measures analysis of variance (rmANOVA) was used to examine the nutrient content of sampled guano piles. Tests were conducted examining samples from all piles, excluding Maternity Roost, at each time period and with only the three more active piles. To meet the requirements for rmANOVA, tests were conducted examining all four time periods excluding Maternity roost (MR) samples and including samples from all piles but excluding samples from September 1, 2010.

Degrees F Nutrient Compairson of P statistic freedom All times, excluding MR F(3,12) 0.7130 0.5684 Carbon Excluding KT and SS and sample time 9/2010 F(2,2) 0.2831 0.7674 All times, excluding MR F(3,12) 0.6209 0.6190 Nitrogen Excluding KT and SS and sample time 9/2010 F(2,2) 0.2684 0.7733 All times, excluding MR F(3,12) 0.5997 0.6312 Phosphorus Excluding KT and SS and sample time 9/2010 F(2,2) 1.1460 0.3788 All times, excluding MR F(3,12) 2.1610 0.1626 Calcium Excluding KT and SS and sample time 9/2010 F(2,2) 3.4250 0.1018 All times, excluding MR F(3,12) 1.2560 0.3463 Magnesium Excluding KT and SS and sample time 9/2010 F(2,2) 1.2060 0.3628 All times, excluding MR F(3,12) 0.2894 0.8321 Potassium Excluding KT and SS and sample time 9/2010 F(2,2) 0.5415 0.6079 All times, excluding MR F(3,12) 0.8630 0.4948 Sodium Excluding KT and SS and sample time 9/2010 F(2,2) 0.1154 0.8930

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Supp Table 1. Sequencing summaries for individual samples, samples sites by collection date, each pile separately, and for the total study.

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Supp. Table 2. Pyrosequencing fusion primer sequences used in this study and their associates samples.

Primer Unique multiplex Corresponding Pyroseq forward primer "A" KEY Sequence specific primer Name ID Tag sample KPITS1F1 CGT ATC GCC TCC CTC GCG CCA TCAG ACGAGTGCGT CTT GGT CAT TTA GAG GAA GTA A SS 2-2010 KPITS1F2 CGT ATC GCC TCC CTC GCG CCA TCAG ACGCTCGACA CTT GGT CAT TTA GAG GAA GTA A KT 4-2010 KPITS1F3 CGT ATC GCC TCC CTC GCG CCA TCAG AGACGCACTC CTT GGT CAT TTA GAG GAA GTA A Som 4-2010 KPITS1F4 CGT ATC GCC TCC CTC GCG CCA TCAG AGCACTGTAG CTT GGT CAT TTA GAG GAA GTA A SS 4-2010 KPITS1F5 CGT ATC GCC TCC CTC GCG CCA TCAG ATCAGACACG CTT GGT CAT TTA GAG GAA GTA A MR 4-2010 KPITS1F6 CGT ATC GCC TCC CTC GCG CCA TCAG ATATCGCGAG CTT GGT CAT TTA GAG GAA GTA A LS 4-2010 KPITS1F7 CGT ATC GCC TCC CTC GCG CCA TCAG CGTGTCTCTA CTT GGT CAT TTA GAG GAA GTA A SS 9-2010 KPITS1F8 CGT ATC GCC TCC CTC GCG CCA TCAG CTCGCGTGTC CTT GGT CAT TTA GAG GAA GTA A KT 9-2010 KPITS1F9 CGT ATC GCC TCC CTC GCG CCA TCAG TAGTATCAGC CTT GGT CAT TTA GAG GAA GTA A Som 9-2010 KPITS1F10 CGT ATC GCC TCC CTC GCG CCA TCAG TCTCTATGCG CTT GGT CAT TTA GAG GAA GTA A LS 9-2010 KPITS1F12 CGT ATC GCC TCC CTC GCG CCA TCAG TACTGAGCTA CTT GGT CAT TTA GAG GAA GTA A SS 12-2010 KPITS1F13 CGT ATC GCC TCC CTC GCG CCA TCAG CATAGTAGTG CTT GGT CAT TTA GAG GAA GTA A KT 12-2010 KPITS1F14 CGT ATC GCC TCC CTC GCG CCA TCAG CGAGAGATAC CTT GGT CAT TTA GAG GAA GTA A MR 12-2010 KPITS1F15 CGT ATC GCC TCC CTC GCG CCA TCAG CACATGACTG CTT GGT CAT TTA GAG GAA GTA A Som 12-2010 KPITS1F16 CGT ATC GCC TCC CTC GCG CCA TCAG CATCTCAGTC CTT GGT CAT TTA GAG GAA GTA A LS 12-2010 KPITS1F18 CGT ATC GCC TCC CTC GCG CCA TCAG GACGATCGTA CTT GGT CAT TTA GAG GAA GTA A SS 4-2011 KPITS1F19 CGT ATC GCC TCC CTC GCG CCA TCAG CTCAGATATA CTT GGT CAT TTA GAG GAA GTA A KT 4-2011 KPITS1F21 CGT ATC GCC TCC CTC GCG CCA TCAG AGAGCTACAG CTT GGT CAT TTA GAG GAA GTA A MR 4-2011 KPITS1F22 CGT ATC GCC TCC CTC GCG CCA TCAG CGTACTGTCT CTT GGT CAT TTA GAG GAA GTA A Som 4-2011 KPITS1F23 CGT ATC GCC TCC CTC GCG CCA TCAG GTCGTCACAC CTT GGT CAT TTA GAG GAA GTA A LS 4-2011

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Supp. Table 3 Results for the nutrient analysis of the 19 guano samples analyzed in this study. Nutrients are reported as total percentages per sample.

Sample Percent total Total Total Total Total Total Total Total Source Date C N P Ca Mg K Na Kartchner towers Apr-10 34.52 6.92 0.73 4.02 0.21 0.30 0.02 Kartchner towers Sep-10 27.44 5.70 1.30 5.71 0.26 0.47 0.02 Kartchner towers Dec-10 27.83 5.92 0.96 6.07 0.22 0.41 0.02 Kartchner towers Apr-11 9.26 1.75 5.69 15.41 0.75 0.59 0.09 Lunch spot Apr-10 45.37 11.44 0.70 0.73 0.24 0.52 0.09 Lunch spot Sep-10 44.34 11.21 0.66 0.71 0.20 0.55 0.07 Lunch spot Dec-10 44.15 11.83 0.72 0.49 0.26 0.66 0.09 Lunch spot Apr-11 45.91 10.41 0.45 1.11 0.19 0.38 0.06 Maternity roost Apr-10 41.42 10.56 0.82 1.13 0.24 0.56 0.09 Maternity roost Dec-10 47.42 10.95 0.37 0.27 0.13 0.36 0.05 Maternity roost Apr-11 29.99 7.37 3.89 7.88 1.39 0.34 0.09 Sombrero Apr-10 46.84 10.89 0.59 0.85 0.21 0.48 0.08 Sombrero Sep-10 42.30 10.04 0.67 1.57 0.22 0.38 0.06 Sombrero Dec-10 29.65 7.32 0.52 0.96 0.18 0.49 0.07 Sombrero Apr-11 44.00 10.82 0.82 1.65 0.25 0.52 0.08 Sharon’s Saddle Apr-10 35.93 6.51 0.98 5.04 0.07 0.16 0.01 Sharon’s Saddle Sep-10 37.08 6.90 0.45 3.76 0.07 0.16 0.01 Sharon’s Saddle Dec-10 41.87 8.20 0.35 3.31 0.06 0.14 0.01 Sharon’s Saddle Apr-11 38.16 6.85 0.48 3.18 0.08 0.16 0.02

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Figure legends

Figure 1. This map of Kartchner Caverns depicts the relative positions of the guano piles that are the focus of this study. All of the guano piles are located in the Big

Room Complex beneath maternity roosting sites for M. velifer.

Figure 2. Sampling completeness was examined using a) a sample based accumulation curve for fungal OTU (95% similarity) recovery from all guano samples. The bootstrap estimate of total OTU richness lies outside of the 95% confidence interval for the observed OTU accumulation, indicating sampling was not complete. OTU rarefaction b) between the sample with the lowest number of OTUs and the highest revealed that there was no significant difference in OTU recovery among piles.

Figure 3. Samples were plotted using Non-metric multidimensional scaling (NMDS), which uses measures of community similarity between samples to place those samples into two dimensional space based on their rank differences. The NMDS plots revealed that when samples were grouped by a) sample time, the area occupied by those clusters overlapped. When grouped by b) sample origin, distinct clusters were formed for the Kartchner towers guano pile (KT), Sharon’s Saddle pile (SS), and the Maternity Roost (MR), Lunch Spot (LS), and Sombrero (Som) piles.

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Figure 4. Bar charts composed of each represented fungal class as a percentage of the total sequences examined for each time period or among piles. No differences were found when comparing the distribution of fungal OTUs to represented fungal classes among a) sampled times (pairwise Chi2, Table 2). However, when examining the distribution of fungal OTUs to fungal class among b) sampled piles, each pairwise pile combination was significantly different except for Lunch Spot (LS) vs. Maternity

Roost (MR), LS vs. Sombrero (Som), and Som vs. Sharon’s Saddle (SS). Kartchner towers (KT) had a unique distribution of fungal OTU classes.

Figure 5. The principal component analysis (PCA) was conducted to examine the relationships among measured nutrient concentrations and sample source. PCA biplot lines represent increasing concentration of the indicated nutrient. The first axis

(96.73 % of variation) was positively correlated with total nitrogen and sodium. The second axis (2.39% of total variation) was positively correlated with total magnesium, phosphorus, potassium, and calcium, and negatively correlated with total carbon. The position of the sites and the associated pile from which the guano was collected along the biplot nutrient lines indicates the level of that nutrient in that sample as compared to the other samples analyzed. When convex hulls are applied to the biplot based on a) sampling time, the resulting clusters overlap. When the hulls are applied based on b) guano pile, two distinct groups emerge that correspond to pile activity.

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Figure 6. CCA triplots were constructed to test whether observed environmental variables had a significant effect on observed community composition. The triplot lines represent relative concentration of the indicated nutrient. The first axis was correlated with total nitrogen, sodium, calcium, and potassium. Sampling sites were separated into a group consisting of samples from Sombrero, Maternity roost (MR), and Lunch Spot (LS), and a group containing samples from Sharon’s Saddle and

Kartchner towers. The former group was further separated by the second axis, which was correlated with total carbon, magnesium, and phosphorus.

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